cloud-complete.ppt

Bharat Bhargava
bbshail@purdue.edu
Computer Science
Purdue University
Research in Cloud Security and Privacy
YounSun Cho
cho52@cs.purdue.edu
Computer Science
Purdue University
Anya Kim
anya.kim@nrl.navy.mil
Naval Research Lab

Talk Objectives
• A high-level discussion of the fundamental challenges
and issues/characteristics of cloud computing
• Identify a few security and privacy issues within this
framework
• Propose some approaches to addressing these issues
– Preliminary ideas to think about

Outline
• Part I: Introduction
• Part II: Security and Privacy Issues in Cloud Computing
• Part III: Possible Solutions
3

Part I. Introduction
• Cloud Computing Background
• Cloud Models
• Why do you still hesitate to use cloud computing?
• Causes of Problems Associated with Cloud Computing
• Taxonomy of Fear
• Threat Model
4

Cloud Computing Background
• Features
– Use of internet-based services to support business process
– Rent IT-services on a utility-like basis
• Attributes
– Rapid deployment
– Low startup costs/ capital investments
– Costs based on usage or subscription
– Multi-tenant sharing of services/ resources
• Essential characteristics
– On demand self-service
– Ubiquitous network access
– Location independent resource pooling
– Rapid elasticity
– Measured service
• “Cloud computing is a compilation of existing techniques and
technologies, packaged within a new infrastructure paradigm that
offers improved scalability, elasticity, business agility, faster startup
time, reduced management costs, and just-in-time availability of
resources”
From [1] NIST

A Massive Concentration of Resources
• Also a massive concentration of risk
– expected loss from a single breach can be significantly
larger
– concentration of “users” represents a concentration of
threats
• “Ultimately, you can outsource responsibility but you can’t
outsource accountability.”
From [2] John McDermott, ACSAC 09

Cloud Computing: who should use it?
• Cloud computing definitely makes sense if your own security
is weak, missing features, or below average.
• Ultimately, if
– the cloud provider’s security people are “better” than
yours (and leveraged at least as efficiently),
– the web-services interfaces don’t introduce too many
new vulnerabilities, and
– the cloud provider aims at least as high as you do, at
security goals,
then cloud computing has better security.
From [2] John McDermott, ACSAC 09

Cloud Models
• Delivery Models
– SaaS
– PaaS
– IaaS
• Deployment Models
– Private cloud
– Community cloud
– Public cloud
– Hybrid cloud
• We propose one more Model: Management Models
(trust and tenancy issues)
– Self-managed
– 3rd party managed (e.g. public clouds and VPC)
From [1] NIST

Delivery Models
9
While cloud-based software services are maturing,
Cloud platform and infrastructure offering are still in their early stages !
From [6] Cloud Security and Privacy by Mather and Kumaraswamy

Impact of cloud computing on the governance
structure of IT organizations
10

If cloud computing is so great,
why isn’t everyone doing it?
• The cloud acts as a big black box, nothing inside the
cloud is visible to the clients
• Clients have no idea or control over what happens
inside a cloud
• Even if the cloud provider is honest, it can have
malicious system admins who can tamper with the
VMs and violate confidentiality and integrity
• Clouds are still subject to traditional data
confidentiality, integrity, availability, and privacy
issues, plus some additional attacks
11

Companies are still afraid to use clouds
12
[Chow09ccsw]

Causes of Problems Associated
with Cloud Computing
• Most security problems stem from:
– Loss of control
– Lack of trust (mechanisms)
– Multi-tenancy
• These problems exist mainly in 3rd party
management models
– Self-managed clouds still have security issues, but
not related to above

Loss of Control in the Cloud
• Consumer’s loss of control
– Data, applications, resources are located with
provider
– User identity management is handled by the cloud
– User access control rules, security policies and
enforcement are managed by the cloud provider
– Consumer relies on provider to ensure
• Data security and privacy
• Resource availability
• Monitoring and repairing of services/resources

Lack of Trust in the Cloud
• A brief deviation from the talk
– (But still related)
– Trusting a third party requires taking risks
• Defining trust and risk
– Opposite sides of the same coin (J. Camp)
– People only trust when it pays (Economist’s view)
– Need for trust arises only in risky situations
• Defunct third party management schemes
– Hard to balance trust and risk
– e.g. Key Escrow (Clipper chip)
– Is the cloud headed toward the same path?

Multi-tenancy Issues in the Cloud
• Conflict between tenants’ opposing goals
– Tenants share a pool of resources and have opposing goals
• How does multi-tenancy deal with conflict of interest?
– Can tenants get along together and ‘play nicely’ ?
– If they can’t, can we isolate them?
• How to provide separation between tenants?
• Cloud Computing brings new threats
– Multiple independent users share the same physical
infrastructure
– Thus an attacker can legitimately be in the same physical
machine as the target

Taxonomy of Fear
• Confidentiality
– Fear of loss of control over data
• Will the sensitive data stored on a cloud remain
confidential?
• Will cloud compromises leak confidential client data
– Will the cloud provider itself be honest and won’t
peek into the data?
• Integrity
– How do I know that the cloud provider is doing the
computations correctly?
– How do I ensure that the cloud provider really
stored my data without tampering with it?
17
From [5] www.cs.jhu.edu/~ragib/sp10/cs412

Taxonomy of Fear (cont.)
• Availability
– Will critical systems go down at the client, if the
provider is attacked in a Denial of Service attack?
– What happens if cloud provider goes out of
business?
– Would cloud scale well-enough?
– Often-voiced concern
• Although cloud providers argue their downtime compares
well with cloud user’s own data centers
18

• Privacy issues raised via massive data mining
– Cloud now stores data from a lot of clients, and can
run data mining algorithms to get large amounts of
information on clients
• Increased attack surface
– Entity outside the organization now stores and
computes data, and so
– Attackers can now target the communication link
between cloud provider and client
– Cloud provider employees can be phished
19

• Auditability and forensics (out of control of data)
– Difficult to audit data held outside organization in
a cloud
– Forensics also made difficult since now clients
don’t maintain data locally
• Legal quagmire and transitive trust issues
– Who is responsible for complying with regulations?
• e.g., SOX, HIPAA, GLBA ?
– If cloud provider subcontracts to third party
clouds, will the data still be secure?
20

21
Cloud Computing is a security
nightmare and it can't be handled
in traditional ways.
John Chambers
CISCO CEO
• Security is one of the most difficult task to implement in
cloud computing.
– Different forms of attacks in the application side and
in the hardware components
• Attacks with catastrophic effects only needs one
security flaw
(http://www.exforsys.com/tutorials/cloud-computing/cloud-computing-security.html)

Threat Model
• A threat model helps in analyzing a security problem,
design mitigation strategies, and evaluate solutions
•Steps:
– Identify attackers, assets, threats and other
components
– Rank the threats
– Choose mitigation strategies
– Build solutions based on the strategies
22

Threat Model
• Basic components
– Attacker modeling
• Choose what attacker to consider
– insider vs. outsider?
– single vs. collaborator?
• Attacker motivation and capabilities
– Attacker goals
– Vulnerabilities / threats
23

What is the issue?
• The core issue here is the levels of trust
– Many cloud computing providers trust their
customers
– Each customer is physically commingling its data
with data from anybody else using the cloud while
logically and virtually you have your own space
– The way that the cloud provider implements
security is typically focused on they fact that
those outside of their cloud are evil, and those
inside are good.
• But what if those inside are also evil?
24

Attacker Capability: Malicious Insiders
• At client
– Learn passwords/authentication information
– Gain control of the VMs
• At cloud provider
– Log client communication
– Can read unencrypted data
– Can possibly peek into VMs, or make copies of VMs
– Can monitor network communication, application
patterns
– Why?
• Gain information about client data
• Gain information on client behavior
• Sell the information or use itself
25

Attacker Capability: Outside attacker
• What?
– Listen to network traffic (passive)
– Insert malicious traffic (active)
– Probe cloud structure (active)
– Launch DoS
• Goal?
– Intrusion
– Network analysis
– Man in the middle
– Cartography
26

Challenges for the attacker
• How to find out where the target is located?
• How to be co-located with the target in the same
(physical) machine?
• How to gather information about the target?
27

Part II: Security and Privacy Issues
in Cloud Computing - Big Picture
• Infrastructure Security
• Data Security and Storage
• Identity and Access Management (IAM)
• Privacy
• And more…
28

Infrastructure Security
• Network Level
• Host Level
• Application Level
29

The Network Level
• Ensuring confidentiality and integrity of your
organization’s data-in-transit to and from your public
cloud provider
• Ensuring proper access control (authentication,
authorization, and auditing) to whatever resources
you are using at your public cloud provider
• Ensuring availability of the Internet-facing resources
in a public cloud that are being used by your
organization, or have been assigned to your
organization by your public cloud providers
• Replacing the established model of network zones and
tiers with domains
30

The Network Level - Mitigation
• Note that network-level risks exist regardless of
what aspects of “cloud computing” services are being
used
• The primary determination of risk level is therefore
not which *aaS is being used,
• But rather whether your organization intends to use
or is using a public, private, or hybrid cloud.
31

The Host Level
• SaaS/PaaS
– Both the PaaS and SaaS platforms abstract and
hide the host OS from end users
– Host security responsibilities are transferred to
the CSP (Cloud Service Provider)
• You do not have to worry about protecting hosts
– However, as a customer, you still own the risk of
managing information hosted in the cloud services.
32

The Host Level (cont.)
• IaaS Host Security
– Virtualization Software Security
• Hypervisor (also called Virtual Machine Manager (VMM)) security is
a key
– a small application that runs on top of the physical machine
H/W layer
– implements and manages the virtual CPU, virtual memory, event
channels, and memory shared by the resident VMs
– Also controls I/O and memory access to devices.
• Bigger problem in multitenant architectures
– Customer guest OS or Virtual Server Security
• The virtual instance of an OS
• Vulnerabilities have appeared in virtual instance of an OS
• e.g., VMWare, Xen, and Microsoft’s Virtual PC and Virtual Server
• Customers have full access to virtual servers.
33

Case study: Amazon's EC2 infrastructure
• “Hey, You, Get Off of My Cloud: Exploring Information Leakage in
Third-Party Compute Clouds”
– Multiple VMs of different organizations with virtual boundaries
separating each VM can run within one physical server
– "virtual machines" still have internet protocol, or IP, addresses,
visible to anyone within the cloud.
– VMs located on the same physical server tend to have IP
addresses that are close to each other and are assigned at the
same time
– An attacker can set up lots of his own virtual machines, look at
their IP addresses, and figure out which one shares the same
physical resources as an intended target
– Once the malicious virtual machine is placed on the same server
as its target, it is possible to carefully monitor how access to
resources fluctuates and thereby potentially glean sensitive
information about the victim
34

Local Host Security
• Are local host machines part of the cloud infrastructure?
– Outside the security perimeter
– While cloud consumers worry about the security on the cloud
provider’s site, they may easily forget to harden their own
machines
• The lack of security of local devices can
– Provide a way for malicious services on the cloud to attack
local networks through these terminal devices
– Compromise the cloud and its resources for other users

Local Host Security (Cont.)
• With mobile devices, the threat may be even stronger
– Users misplace or have the device stolen from them
– Security mechanisms on handheld gadgets are often times
insufficient compared to say, a desktop computer
– Provides a potential attacker an easy avenue into a cloud
system.
– If a user relies mainly on a mobile device to access cloud
data, the threat to availability is also increased as mobile
devices malfunction or are lost
• Devices that access the cloud should have
– Strong authentication mechanisms
– Tamper-resistant mechanisms
– Strong isolation between applications
– Methods to trust the OS
– Cryptographic functionality when traffic confidentiality is
required
36

The Application Level
• DoS
• EDoS(Economic Denial of Sustainability)
– An attack against the billing model that underlies
the cost of providing a service with the goal of
bankrupting the service itself.
• End user security
• Who is responsible for Web application security in
the cloud?
• SaaS/PaaS/IaaS application security
• Customer-deployed application security
37

Data Security and Storage
• Several aspects of data security, including:
– Data-in-transit
• Confidentiality + integrity using secured protocol
• Confidentiality with non-secured protocol and encryption
– Data-at-rest
• Generally, not encrypted , since data is commingled with
other users’ data
• Encryption if it is not associated with applications?
– But how about indexing and searching?
– Then homomorphic encryption vs. predicate
encryption?
– Processing of data, including multitenancy
• For any application to process data, not encrypted
38

Data Security and Storage (cont.)
– Data lineage
• Knowing when and where the data was located w/i cloud is
important for audit/compliance purposes
• e.g., Amazon AWS
– Store <d1, t1, ex1.s3.amazonaws.com>
– Process <d2, t2, ec2.compute2.amazonaws.com>
– Restore <d3, t3, ex2.s3.amazonaws.com>
– Data provenance
• Computational accuracy (as well as data integrity)
• E.g., financial calculation: sum ((((2*3)*4)/6) -2) = $2.00 ?
– Correct : assuming US dollar
– How about dollars of different countries?
– Correct exchange rate?
39
Where is (or was) that system located?
What was the state of that physical system?
How would a customer or auditor verify that info?

Data Security and Storage
• Data remanence
– Inadvertent disclosure of sensitive information is possible
• Data security mitigation?
– Do not place any sensitive data in a public cloud
– Encrypted data is placed into the cloud?
• Provider data and its security: storage
– To the extent that quantities of data from many
companies are centralized, this collection can become an
attractive target for criminals
– Moreover, the physical security of the data center and
the trustworthiness of system administrators take on new
importance.
40

Why IAM?
• Organization’s trust boundary will become dynamic and will move
beyond the control and will extend into the service provider
domain.
• Managing access for diverse user populations (employees,
contractors, partners, etc.)
• Increased demand for authentication
– personal, financial, medical data will now be hosted in the
cloud
– S/W applications hosted in the cloud requires access control
• Need for higher-assurance authentication
– authentication in the cloud may mean authentication outside
F/W
– Limits of password authentication
• Need for authentication from mobile devices
41

IAM considerations
• The strength of authentication system should be
reasonably balanced with the need to protect the privacy
of the users of the system
– The system should allow strong claims to be
transmitted and verified w/o revealing more
information than is necessary for any given transaction
or connection within the service
• Case Study: S3 outage
– authentication service overload leading to unavailability
• 2 hours 2/15/08
• http://www.centernetworks.com/amazon-s3-downtime-
update
42

What is Privacy?
• The concept of privacy varies widely among (and
sometimes within) countries, cultures, and jurisdictions.
• It is shaped by public expectations and legal
interpretations; as such, a concise definition is elusive if
not impossible.
• Privacy rights or obligations are related to the collection,
use, disclosure, storage, and destruction of personal data
(or Personally Identifiable Information—PII).
• At the end of the day, privacy is about the accountability
of organizations to data subjects, as well as the
transparency to an organization’s practice around personal
information.
43

What is the data life cycle?
44
• Personal information should be
managed as part of the data used by
the organization
• Protection of personal information
should consider the impact of the
cloud on each phase

What Are the Key Privacy Concerns?
• Typically mix security and privacy
• Some considerations to be aware of:
– Storage
– Retention
– Destruction
– Auditing, monitoring and risk management
– Privacy breaches
– Who is responsible for protecting privacy?
45

Storage
• Is it commingled with information from other
organizations that use the same CSP?
• The aggregation of data raises new privacy issues
– Some governments may decide to search through
data without necessarily notifying the data owner,
depending on where the data resides
• Whether the cloud provider itself has any right to
see and access customer data?
• Some services today track user behaviour for a range
of purposes, from sending targeted advertising to
improving services
46

Retention
• How long is personal information (that is transferred
to the cloud) retained?
• Which retention policy governs the data?
• Does the organization own the data, or the CSP?
• Who enforces the retention policy in the cloud, and
how are exceptions to this policy (such as litigation
holds) managed?
47

Destruction
• How does the cloud provider destroy PII at the end of the
retention period?
• How do organizations ensure that their PII is destroyed by
the CSP at the right point and is not available to other cloud
users?
• Cloud storage providers usually replicate the data across
multiple systems and sites—increased availability is one of
the benefits they provide.
– How do you know that the CSP didn’t retain additional
copies?
– Did the CSP really destroy the data, or just make it
inaccessible to the organization?
– Is the CSP keeping the information longer than necessary
so that it can mine the data for its own use?
48

Auditing, monitoring and risk management
• How can organizations monitor their CSP and provide
assurance to relevant stakeholders that privacy
requirements are met when their PII is in the cloud?
• Are they regularly audited?
• What happens in the event of an incident?
• If business-critical processes are migrated to a cloud
computing model, internal security processes need to
evolve to allow multiple cloud providers to participate
in those processes, as needed.
– These include processes such as security
monitoring, auditing, forensics, incident response,
and business continuity
49

Privacy breaches
• How do you know that a breach has occurred?
• How do you ensure that the CSP notifies you when a
breach occurs?
• Who is responsible for managing the breach
notification process (and costs associated with the
process)?
• If contracts include liability for breaches resulting
from negligence of the CSP?
– How is the contract enforced?
– How is it determined who is at fault?
50

Who is responsible for protecting privacy?
• Data breaches have a cascading effect
• Full reliance on a third party to protect personal
data?
• In-depth understanding of responsible data
stewardship
• Organizations can transfer liability, but not
accountability
• Risk assessment and mitigation throughout the data
life cycle is critical.
• Many new risks and unknowns
– The overall complexity of privacy protection in the
cloud represents a bigger challenge.
51
e.g., Suppose a hacker breaks into Cloud Provider A and steals data from Company X.
Assume that the compromised server also contained data from Companies Y and Z.
• Who investigates this crime?
• Is it the Cloud Provider, even though Company X may fear that
the provider will try to absolve itself from responsibility?
• Is it Company X and, if so, does it have the right to see other data on that server,
including logs that may show access to the data of Companies Y and Z?

Part III. Possible Solutions
• Minimize Lack of Trust
– Policy Language
– Certification
• Minimize Loss of Control
– Monitoring
– Utilizing different clouds
– Access control management
– Identity Management (IDM)
• Minimize Multi-tenancy
52

Security Issues in the Cloud
• In theory, minimizing any of the issues would help:
– Third Party Cloud Computing
– Loss of Control
• Take back control
– Data and apps may still need to be on the cloud
– But can they be managed in some way by the consumer?
– Lack of trust
• Increase trust (mechanisms)
– Technology
– Policy, regulation
– Contracts (incentives): topic of a future talk
– Multi-tenancy
• Private cloud
– Takes away the reasons to use a cloud in the first place
• VPC: its still not a separate system
• Strong separation

Like Amazon’s EC2, Microsoft’s Azure
• Allow users to instantiate Virtual Machines
• Allow users to purchase required quantity
when required
• Allow service providers to maximize the
utilization of sunk capital costs
• Confidentiality is very important
Third Party Cloud Computing

• Confidentiality issues
• Malicious behavior by cloud provider
• Known risks exist in any industry practicing
outsourcing
• Provider and its infrastructure needs to be trusted
Known issues: Already exist

• Threats arise from other consumers
• Due to the subtleties of how physical resources
can be transparently shared between VMs
• Such attacks are based on placement and
extraction
• A customer VM and its adversary can be assigned
to the same physical server
• Adversary can penetrate the VM and violate
customer confidentiality
New Vulnerabilities & Attacks

• Collaborative attacks
• Mapping of internal cloud infrastructure
• Identifying likely residence of a target VM
• Instantiating new VMs until one gets co-
resident with the target
• Cross-VM side-channel attacks
• Extract information from target VM on the
same machine
More on attacks…

• Can one determine where in the cloud infrastructure
an instance is located?
• Can one easily determine if two instances are co-
resident on the same physical machine?
• Can an adversary launch instances that will be co-
resident with other user instances?
• Can an adversary exploit cross-VM information
leakage once co-resident?
Answer: Yes to all
More on attacks…

- POLICY LANGUAGE
- CERTIFICATION
Minimize Lack of Trust
59

Minimize Lack of Trust:
Policy Language
• Consumers have specific security needs but don’t
have a say-so in how they are handled
– What the heck is the provider doing for me?
– Currently consumers cannot dictate their
requirements to the provider (SLAs are one-sided)
• Standard language to convey one’s policies and
expectations
– Agreed upon and upheld by both parties
– Standard language for representing SLAs
– Can be used in a intra-cloud environment to realize
overarching security posture

Minimize Lack of Trust:
Policy Language (Cont.)
• Create policy language with the following
characteristics:
– Machine-understandable (or at least processable),
– Easy to combine/merge and compare
– Examples of policy statements are, “requires
isolation between VMs”, “requires geographical
isolation between VMs”, “requires physical
separation between other communities/tenants
that are in the same industry,” etc.
– Need a validation tool to check that the policy
created in the standard language correctly
reflects the policy creator’s intentions (i.e. that
the policy language is semantically equivalent to
the user’s intentions).
61

Minimize Lack of Trust: Certification
• Certification
– Some form of reputable, independent, comparable
assessment and description of security features
and assurance
– Sarbanes-Oxley, DIACAP, DISTCAP, etc (are they
sufficient for a cloud environment?)
• Risk assessment
– Performed by certified third parties
– Provides consumers with additional assurance

- MONITORING
- UTILIZING DIFFERENT CLOUDS
- ACCESS CONTROL MANAGEMENT
- IDENTITY MANAGEMENT (IDM)
Minimize Loss of Control
63

Minimize Loss of Control:
Monitoring
• Cloud consumer needs situational awareness for
critical applications
– When underlying components fail, what is the
effect of the failure to the mission logic
– What recovery measures can be taken (by provider
and consumer)
• Requires an application-specific run-time monitoring
and management tool for the consumer
– The cloud consumer and cloud provider have
different views of the system
– Enable both the provider and tenants to monitor
the components in the cloud that are under their
control

Monitoring (Cont.)
65
– Provide mechanisms that enable the provider to
act on attacks he can handle.
• infrastructure remapping (create new or move
existing fault domains)
• shutting down offending components or targets
(and assisting tenants with porting if necessary
• Repairs
– Provide mechanisms that enable the consumer to
act on attacks that he can handle (application-level
monitoring).
• RAdAC (Risk-adaptable Access Control)
• VM porting with remote attestation of target
physical host
• Provide ability to move the user’s application to
another cloud

Utilize Different Clouds
• The concept of ‘Don’t put all your eggs in one basket’
– Consumer may use services from different clouds through an
intra-cloud or multi-cloud architecture
– Propose a multi-cloud or intra-cloud architecture in which
consumers
• Spread the risk
• Increase redundancy (per-task or per-application)
• Increase chance of mission completion for critical applications
– Possible issues to consider:
• Policy incompatibility (combined, what is the overarching policy?)
• Data dependency between clouds
• Differing data semantics across clouds
• Knowing when to utilize the redundancy feature (monitoring
technology)
• Is it worth it to spread your sensitive data across multiple
clouds?
– Redundancy could increase risk of exposure

Access Control
• Many possible layers of access control
– E.g. access to the cloud, access to servers, access to
services, access to databases (direct and queries via web
services), access to VMs, and access to objects within a VM
– Depending on the deployment model used, some of these will
be controlled by the provider and others by the consumer
• Regardless of deployment model, provider needs to
manage the user authentication and access control
procedures (to the cloud)
– Federated Identity Management: access control management
burden still lies with the provider
– Requires user to place a large amount of trust on the
provider in terms of security, management, and maintenance
of access control policies. This can be burdensome when
numerous users from different organizations with different
access control policies, are involved

Access Control (Cont.)
68
• Consumer-managed access control
– Consumer retains decision-making process to retain
some control, requiring less trust of the provider
(i.e. PDP is in consumer’s domain)
– Requires the client and provider to have a pre-
existing trust relationship, as well as a pre-
negotiated standard way of describing resources,
users, and access decisions between the cloud
provider and consumer. It also needs to be able to
guarantee that the provider will uphold the
consumer-side’s access decisions.
– Should be at least as secure as the traditional
access control model.
– Facebook and Google Apps do this to some degree,
but not enough control
– Applicability to privacy of patient health records

PEP
(intercepts all
resource
access requests
from all client
domains)
PDP
for cloud
resource
on Domain A
Cloud Consumer in Domain B
ACM
(XACML
policies)
.
.
.
resources
Cloud Provider in Domain A
IDP
1. Authn request
2. SAML Assertion
3. Resource request (XACML Request) + SAML assertion
4. Redirect to domain of resource owner
7. Send signed and encrypted ticket
5. Retrieve policy
for specified resource
6. Determine whether user can access
specified resource
7. Create ticket for grant/deny
8. Decrypt and verify signature
9. Retrieve capability from ticket
10. Grant or deny access based on capability
Access Control

Minimize Loss of Control: IDM
Motivation
User on Amazon
Cloud
1. Name
2. E-mail
3. Password
4. Billing Address
5. Shipping Address
6. Credit Card
1. Name
2. E-mail
3. Shipping Address
1. Name
2. Billing Address
3. Credit Card
1. Name
2. E-mail
3. Password
4. Billing Address
5. Shipping Address
6. Credit Card
1. Name
2. E-mail
3. Shipping Address

Identity in the Cloud
User on Amazon
Cloud
1. Name
2. E-mail
3. Password
4. Billing Address
5. Shipping Address
6. Credit Card
1. Name
2. E-mail
3. Shipping Address
1. Name
2. Billing Address
3. Credit Card
1. Name
2. E-mail
3. Password
4. Billing Address
5. Shipping Address
6. Credit Card
1. Name
2. E-mail
3. Shipping Address

Present IDMs
• IDM in traditional application-centric IDM model
– Each application keeps track of identifying information of its
users.
• Existing IDM Systems
– Microsoft Windows CardSpace [W. A. Alrodhan]
– OpenID [http://openid.net]
– PRIME [S. F. Hubner]
These systems require a trusted third party and
do not work on an untrusted host.
If Trusted Third Party is compromised, all the identifying information
of the users is also compromised
[Latest: AT&T iPad leak]

Issues in Cloud Computing
• Cloud introduces several issues to IDM
– Users have multiple accounts associated with multiple
service providers.
– Lack of trust
• Use of Trusted Third Party is not an option
• Cloud hosts are untrusted
– Loss of control
• Collusion between Cloud Services
– Sharing sensitive identity information
between services can lead to undesirable
mapping of the identities to the user.
IDM in Cloud needs to be user-centric

Goals of Proposed User-Centric IDM for the Cloud
1. Authenticate without disclosing identifying information
2. Ability to securely use a service while on an untrusted
host (VM on the cloud)
3. Minimal disclosure and minimized risk of disclosure
during communication between user and service provider
(Man in the Middle, Side Channel and Correlation
Attacks)
4. Independence of Trusted Third Party

Approach - 1
• IDM Wallet:
– Use of AB scheme to protect PII from untrusted
hosts.
• Anonymous Identification:
– Use of Zero-knowledge proofing for authentication
of an entity without disclosing its identifier.

Components of Active Bundle (Approach – 1)
• Identity data: Data used during authentication, getting
service, using service (i.e. SSN, Date of Birth).
• Disclosure policy: A set of rules for choosing Identity
data from a set of identities in IDM Wallet.
• Disclosure history: Used for logging and auditing
purposes.
• Negotiation policy: This is Anonymous Identification,
based on the Zero Knowledge Proofing.
• Virtual Machine: Code for protecting data on untrusted
hosts. It enforces the disclosure policies.

Anonymous Identification (Approach – 1)
Anonymous Identification
(Shamir's approach for Credit Cards)
• IdP provides Encrypted Identity Information to the
user and SP.
• SP and User interact
• Both run IdP's public function on the certain bits of
the Encrypted data.
• Both exchange results and agree if it matches.

Usage Scenario (Approach – 1)

Approach - 2
• Active Bundle scheme to protect PII from untrusted
hosts
• Predicates over encrypted data to authenticate
without disclosing unencrypted identity data.
• Multi-party computing to be independent of a
trusted third party

Usage Scenario (Approach – 2)
• Owner O encrypts Identity Data(PII) using algorithm
Encrypt and O’s public key PK. Encrypt outputs CT—the
encrypted PII.
• SP transforms his request for PII to a predicate
represented by function p.
• SP sends shares of p to the n parties who hold the
shares of MSK.
• n parties execute together KeyGen using PK, MSK, and
p, and return TKp to SP.
• SP calls the algorithm Query that takes as input PK, CT,
TKp and produces p(PII) which is the evaluation of the
predicate.
• The owner O is allowed to use the service only when the
predicate evaluates to “true”.

Representation of identity information
for negotiation
Token/Pseudonym
Identity Information in clear plain text
 Active Bundle

Motivation-Authentication Process using PII
Problem: Which information to disclose and how to
disclose it.

Proposed IDM:
Mechanisms
• [16] Protection of Identity Information in Cloud Computing without
Trusted Third Party - R. Ranchal, B. Bhargava, L.B. Othmane, L. Lilien, A.
Kim, M. Kang, Third International Workshop on Dependable Network
Computing and Mobile Systems (DNCMS) in conjunction with 29th IEEE
Symposium on Reliable Distributed System (SRDS) 2010
• [17] A User-Centric Approach for Privacy and Identity Management in
Cloud Computing - P. Angin, B. Bhargava, R. Ranchal, N. Singh, L. Lilien,
L.B. Othmane 29th IEEE Symposium on Reliable Distributed System
(SRDS) 2010
• Privacy in Cloud Computing Through Identity Management - B. Bhargava,
N. Singh, A. Sinclair, International Conference on Advances in
Computing and Communication ICACC-11, April, 2011, India.
• Active Bundle
• Anonymous Identification
• Computing Predicates with encrypted data
• Multi-Party Computing
• Selective Disclosure

Proposed IDM:
Active Bundle
• Active bundle (AB)
– An encapsulating mechanism protecting data carried
within it
– Includes data
– Includes metadata used for managing confidentiality
• Both privacy of data and privacy of the whole AB
– Includes Virtual Machine (VM)
• performing a set of operations
• protecting its confidentiality

Proposed IDM:
Active Bundle (Cont.)
• Active Bundles—Operations
– Self-Integrity check
E.g., Uses a hash function
– Evaporation/ Filtering
Self-destroys (a part of) AB’s sensitive data when
threatened with a disclosure
– Apoptosis
Self-destructs AB’s completely
85

Proposed IDM:
Active Bundle Scheme
– Metadata:
• Access control policies
• Data integrity checks
• Dissemination policies
• Life duration
• ID of a trust server
• ID of a security server
• App-dependent information
• …
– Sensitive Data:
• Identity
Information
• ...
– Virtual Machine
(algorithm):
• Interprets metadata
• Checks active bundle integrity
• Enforces access and
dissemination control policies
• …
• E(Name)
• E(E-mail)
• E(Password)
• E(Shipping Address)
• E(Billing Address)
• E(Credit Card)
• …
* E( ) - Encrypted Information

Proposed IDM:
Anonymous Identification
User on Amazon
Cloud
1. E-mail
2. Password
1. E-mail
2. Password
User Request for service
Function f and number k
fk(E-mail, Password) = R
ZKP Interactive Protocol
Authenticated
• Use of Zero-knowledge proofing for user authentication
without disclosing its identifier.

Proposed IDM:
Interaction using Active Bundle
Active
Bundle (AB)
Security Services
Agent (SSA)
Active Bundle Services
User Application
Active Bundle Coordinator
Active Bundle
Creator
Directory
Facilitator
Active Bundle Destination
Trust Evaluation
Agent (TEA)
Audit Services
Agent (ASA)
Active Bundle
AB information disclosure

Proposed IDM:
Predicate over Encrypted Data
• Verification without disclosing unencrypted identity data.
• E-mail
• Password
• E(Name)
• E(Credit Card)
• E(Name)
• E(Billing
Address)
• E(Credit Card)
Predicate Request*
*Age Verification Request
*Credit Card Verification Request

Proposed IDM:
Multi-Party Computing
• To become independent of a trusted third party
• Multiple Services hold shares of the secret key
• Minimize the risk
• E(Name)
• E(Billing
Address)
• E(Credit Card)
Key Management Services
K’
1 K’
2 K’
3 K’
n
Predicate Request
* Decryption of information is handled by the Key Management services

Proposed IDM:
Multi-Party Computing
• To become independent of a trusted third party
• Multiple Services hold shares of the secret key
• Minimize the risk
• Name
• Billing Address
• Credit Card
Key Management Services
K’
1 K’
2 K’
3 K’
n
Predicate Reply*
*Age Verified
*Credit Card Verified

Proposed IDM:
Selective Disclosure
• E-mail
• Password
• E(Name)
• E(Credit Card)
Selective disclosure*
• E(E-mail)
• E(Name)
• E(Shipping
Address)
• User Policies in the Active Bundle dictate dissemination
*e-bay shares the encrypted information based on the user policy

Proposed IDM:
• E-mail
• Password
• E(Name)
• E(Credit Card)
• E-mail
• E(Name)
• E(Shipping
Address)
• User Policies in the Active Bundle dictate dissemination
Decryption handled by Multi-Party Computing as in the previous slides

Proposed IDM:
• E-mail
• E(Name)
• E(Name)
• E(Shipping
Address)
*e-bay seller shares the encrypted information based on the user policy

Proposed IDM:
• E-mail
• E(Name)
Selective disclosure
• Name
• Shipping Address
• Decryption handled by Multi-Party Computing as in the previous slides

Proposed IDM:
• E-mail
• E(Name)
Selective disclosure
• Name
• Shipping Address
• Fed-Ex can now send the package to the user

Proposed IDM:
Identity in the Cloud
User on Amazon
Cloud
1. Name
2. E-mail
3. Password
4. Billing Address
5. Shipping Address
6. Credit Card
1. Name
2. Shipping Address
1. Name
2. Billing Address
3. Credit Card
1. E-mail
2. Password
1. E-mail

Proposed IDM:
Characteristics and Advantages
• Ability to use Identity data on untrusted hosts
• Self Integrity Check
• Integrity compromised- apoptosis or evaporation
• Data should not be on this host
• Independent of Third Party
– Prevents correlation attacks
• Establishes the trust of users in IDM
– Through putting the user in control of who has his
data
– Identity is being used in the process of authentication,
negotiation, and data exchange.
• Minimal disclosure to the SP
– SP receives only necessary information.

Proposed IDM:
Conclusion & Future Work
• Problems with IDM in Cloud Computing
– Collusion of Identity Information
– Prohibited Untrusted Hosts
– Usage of Trusted Third Party
• Proposed Approaches
– IDM based on Anonymous Identification
– IDM based on Predicate over Encrypted data
• Future work
– Develop the prototype, conduct experiments and
evaluate the approach

Minimize Multi-tenancy
• Can’t really force the provider to accept less
tenants
– Can try to increase isolation between tenants
• Strong isolation techniques (VPC to some degree)
– C.f. VM Side channel attacks (T. Ristenpart et al.)
• QoS requirements need to be met
• Policy specification
– Can try to increase trust in the tenants
• Who’s the insider, where’s the security boundary? Who
can I trust?
• Use SLAs to enforce trusted behavior

Conclusion
• Cloud computing is sometimes viewed as a
reincarnation of the classic mainframe client-server
model
– However, resources are ubiquitous, scalable, highly
virtualized
– Contains all the traditional threats, as well as new ones
• In developing solutions to cloud computing security
issues it may be helpful to identify the problems and
approaches in terms of
– Loss of control
– Lack of trust
– Multi-tenancy problems

CLOUD COMPUTING FOR MOBILE
USERS: CAN OFFLOADING
COMPUTATION SAVE ENERGY?

Take Amazon cloud for example.
• store personal data
(Simple Storage Service (S3) )
• perform computations on stored data
(Elastic Compute Cloud (EC2). )

If you want to set up a business.
 low initial capital investment
 shorter start-up time for new services
 lower maintenance and operation costs
 higher utilization through virtualization
 easier disaster recovery

Two main concerns:
 mobile computing are limited energy
 wireless bandwidth

Various studies have identified longer battery
lifetime as the most desired feature of such
systems.
 longer battery life to be more important than all
other features, including cameras or storage.
 short battery life to be the most disliked
characteristic of Apple’s iPhone 3GS
 battery life was the top concern of music phone
users.

 Adopt a new generation of semiconductor technology.
 Avoid wasting energy. (when it is idle, sleep mode)
 Execute programs slowly. (When a processor’s clock
speed doubles, the power consumption nearly
octuples).
 Eliminate computation all together. (offloading these
applications to the cloud).

How to implement a quantitative study. The amount of
energy saved is
S : the speed of cloud to compute C instructions
M : the speed of mobile to compute C instructions
D : the data need to transmit
B : the bandwidth of the wireless Internet

the energy cost per second when the mobile phone is
doing computing
the energy cost per second when the mobile phone is
idle.
the energy cost per second when the mobile is
transmission the data.
i
p
c
p
tr
p

Suppose the server is F times faster—that is, S
= F × M. We can rewrite the formula as
Energy is saved when this formula produces a
positive number. The formula is positive if D/B
is sufficiently small compared with C/M and F
is sufficiently large.

chess game.
A chessboard has 8 × 8 = 64 positions. Each
player controls 16 pieces at the beginning of
the game. Each piece may be in one of the 64
possible locations and needs 6 bits to
represent the location. To represent a chess
game’s current state, it is sufficient to state
that 6 bits × 32 pieces = 192 bits = 24 bytes;
this is smaller than the size of a typical
wireless packet.

The amount of computation for chess is very
large; Claude Shannon and Victor Allis
estimated the complexity of chess to exceed
the number of atoms in the universe. Since the
amount of computation C is extremely large,
and D is very small, chess provides an example
where offloading is beneficial for most wireless
networks.

 regions like national parks
 the basement of a building
 interior of a tunnel,
 subway.
In these cases,
where the value of B in Equation can become
very small or even zero, cloud computing does
not save energy.

There is a fundamental assumption
under-lying this analysis with the client-server
model: Because the server does not already
contain the data, all the data must be sent to
the service provider.
However, cloud computing changes that
assumption: The cloud stores data and performs
computation on it. For example, services like
Amazon S3 can store data, and Amazon EC2 can
be used to perform computation on the data
stored using S3.

Another possible privacy and security solution
is to use a technique called steganography :
 Multimedia content like images and videos
have significant redundancy. This makes it
possible to hide data in multimedia using
steganography.
 Steganographic techniques can be used to
transform the data before storage so that
operations can still be performed on the data.

Performing encryption or steganographic
techniques before sending data to the cloud
requires some additional processing on the
mobile system. So the formula become:

 cloud computing can potentially save energy
for mobile users.
 not all applications are energy
efficient when migrated to the cloud.
 cloud computing services would be
significantly different from cloud services for
desktops because they must offer energy
savings.
 The services should consider the energy
overhead for privacy, security, reliability,
and data communication before offloading.

Bandwidth Measurements
for VMs in Cloud

MOTIVATION
• Many applications are being deployed in cloud to leverage
the scalability provided by the cloud providers.
• Tools provided by the cloud providers do not give
performance metrics from the network perspective.
• Network topology is not exposed to the cloud users and the
applications consider all network links to be homogeneous.
• Metrics such as available bandwidth, latency etc. will be
more useful to the cloud users.

Experimental Evaluation
• Set up
o 19 EC2 small instances (US East)
o 342 links between VMs
o Ubuntu 10.04 server version
• Centralized Scheduler for starting Iperf clients
o Predefined serialized schedule file at each VM instance.
o Schedule file contains a time stamp along with the nodes that should
communicate for a single reading.
* Iperf - Network testing tool to measure the network
throughput between end hosts.

Experimental Evaluation
• Iperf takes 6 seconds to get a reading for a single link.
• Each round of measurement takes around 30 minutes for
finding available bandwidth for all 342 links.
• Total 5 rounds in total
• Throughput matrix: Matrix containing estimated values for
available bandwidth

Bandwidth Estimation
• Shows the CDF of link
bandwidth estimation for all
the rounds.
• Used throughput matrix
having estimated 342
values.
• All links in clouds are
not homogeneous.
• Only 10% of the links have
available bandwidth less
than 400Mbps.

Bandwidth Variation Estimation
• Shows the CDF of link
bandwidth variation across
all the rounds.
• Bandwidth range of a link
defined as the difference
between the max and min
value across all rounds.
• For most of the links,
bandwidth is consistent
across time. Only 20%
links have variation of
more than 200 Mbps.

Virtual Machine Performance
• Shows the available
download/upload
bandwidth of all machines
for a single round
• Almost all the machines
have average available
bandwidth more than 400
Mbps.

Virtual Machine Performance
• Shows the average
available download/ upload
bandwidth and its range for
each machine across all
rounds.
• Almost all the machines
have average download/
upload bandwidth more
than 400 Mbps.
• Some VMs (1, 4, 7) have
large available bandwidth
variation.

CONCLUSIONS
• Focussed on available bandwidth metric between each pair
of VM instances.
• Amazon EC2 data center is optimally utilized with ample
available bandwidth for almost all VMs.
• Some badly performing VMs can be pointed out based on
the large variation in the available upload/download
bandwidth and can be replaced with new VMs.

Future Work
• More performance metric such as latency etc. can be
considered.
• These performance metrics can be used to improve the
performance of applications running in the cloud.
• These performance metric tests can be run on large EC2
instances.

A Mobile-Cloud Collaborative
Approach for Context-Aware Blind
Navigation

Outline
• Problem Statement
• Goals
• Challenges
• Context-aware Navigation Components
• Existing Blind Navigation Aids
• Proposed System Architecture
• Advantages of Mobile-Cloud Approach
• Traffic Lights Detection
– Related Work
– System Developed
– Experiments
• Work In Progress

Problem Statement
• Indoor and outdoor navigation is becoming a
harder task for blind and visually impaired people
in the increasingly complex urban world
• Advances in technology are causing the blind to
fall behind, sometimes even putting their lives at
risk
• Technology available for context-aware navigation
of the blind is not sufficiently accessible; some
devices rely heavily on infrastructural
requirements

Demographics
• 314 million visually impaired people in the world
today
• 45 million blind
• More than 82% of the visually impaired
population is age 50 or older
• The old population forms a group with diverse
range of abilities
• The disabled are seldom seen using the street
alone or public transportation

Goals
• ***Make a difference***
Bring mobile technology in the daily lives of blind
and visually impaired people to help achieve a
higher standard of life
• Take a major step in context-aware navigation of
the blind and visually impaired
• Bridge the gap between the needs and available
technology
• Guide users in a non-overwhelming way
• Protect user privacy

Challenges
• Real-time guidance
• Portability
• Power limitations
• Appropriate interface
• Privacy preservation
• Continuous availability
• No dependence on infrastructure
• Low-cost solution
• Minimal training

Discussions
• Cary Supalo: Founder of Independence Science LLC
(http://www.independencescience.com/)
• T.V. Raman: Researcher at Google, leader of Eyes-
Free project (speech enabled Android applications)
• American Council of the Blind of Indiana State
Convention, 31 October 2009
• Miami Lighthouse Organization

Mobility Requirements
• Being able to avoid obstacles
• Walking in the right direction
• Safely crossing the road
• Knowing when you have reached a destination
• Knowing which is the right bus/train
• Knowing when to get off the bus/train
All require SIGHT as primary sense

Context-Aware Navigation Components
• Outdoor Navigation (finding curbs -including in
snow, using public transportation, interpreting
traffic patterns/signal lights…)
• Indoor Navigation (finding stairs/elevator,
specific offices, restrooms in unfamiliar
buildings, finding the cheapest TV at a store…)
• Obstacle Avoidance (both overhanging and low
obstacles…)
• Object Recognition (being able to reach objects
needed, recognizing people who are in the
immediate neighborhood…)

Existing Blind Navigation Aids –
Outdoor Navigation
• Loadstone GPS (http://www.loadstone-gps.com/)
• Wayfinder Access
(http://www.wayfinderaccess.com/)
• BrailleNote GPS (www.humanware.com)
• Trekker (www.humanware.com)
• StreetTalk (www.freedomscientific.com)
• DRISHTI [1]
• …

Indoor Navigation
• InfoGrid (based on RFID) [2]
• Jerusalem College of Technology system (based on
local infrared beams) [3]
• Talking Signs (www.talkingsigns.com) (audio signals
sent by invisible infrared light beams)
• SWAN (audio interface guiding user along path,
announcing important features) [4]
• ShopTalk (for grocery shopping) [5]

Obstacle Avoidance
• RADAR/LIDAR
• Kay’s Sonic glasses (audio for 3D representation
of environment) (www.batforblind.co.nz)
• Sonic Pathfinder (www.sonicpathfinder.org) (notes
of musical scale to warn of obstacles)
• MiniGuide (www.gdp-research.com.au/) (vibration
to indicate object distance)
• VOICE (www.seeingwithsound.com) (images into
sounds heard from 3D auditory display)
• Tactile tongue display [6]
• …

Putting all together…
Gill, J. Assistive Devices for People with Visual Impairments.
In A. Helal, M. Mokhtari and B. Abdulrazak, ed., The Engineering Handbook of Smart Technology for Aging, Disability and Indepen
John Wiley & Sons, Hoboken, New Jersey, 2008.

Proposed System Architecture
Services:
• Google Maps (outdoor navigation, pedestrian
mode)
• Micello (indoor location-based service for mobile
devices)
• Object recognition (Selectin software etc)
• Traffic assistance
• Obstacle avoidance (Time-of-flight camera
technology)
• Speech interface (Android text-to-speech +
speech recognition servers)
• Remote vision
• Obstacle minimized route planning

Advantages of a Mobile-Cloud Collaborative
Approach
• Open architecture
• Extensibility
• Computational power
• Battery life
• Light weight
• Wealth of context-relevant information
resources
• Interface options
• Minimal reliance on infrastructural requirements

Traffic Lights Status Detection Problem
• Ability to detect status of traffic lights
accurately is an important aspect of safe
navigation
– Color blind
– Autonomous ground vehicles
– Careless drivers
• Inherent difficulty: Fast image processing
required for locating and detecting the lights
status  demanding in terms of computational
resources
• Mobile devices with limited resources fall short
alone

Attempts to Solve the Traffic Lights
Detection Problem
• Kim et al: Digital camera + portable PC analyzing
video frames captured by the camera [7]
• Charette et al: 2.9 GHz desktop computer to
process video frames in real time[8]
• Ess et al: Detect generic moving objects with
400 ms video processing time on dual core 2.66
GHz computer[9]
Sacrifice portability for real-time,
accurate detection

Mobile-Cloud Collaborative Traffic Lights
Detector

Adaboost Object Detector
• Adaboost: Adaptive Machine Learning algorithm used
commonly in real-time object recognition
• Based on rounds of calls to weak classifiers to focus
more on incorrectly classified samples at each stage
• Traffic lights detector: trained on 219 images of
traffic lights (Google Images)
• OpenCV library implementation

Experiments: Response time
520
540
560
580
600
620
640
660
response
time(ms)
Frame resolution level
0.75 0.5 0.3 0.1 0.05

Work In Progress
• Develop fully context-aware navigation system with
speech/tactile interface
• Develop robust object/obstacle recognition
algorithms
• Investigate mobile-cloud privacy and security issues
(minimal data disclosure principle) [10]
• Investigate options for mounting of the camera

Collective Object Classification in Complex
Scenes
LabelMe Dataset (http://labelme.csail.mit.edu)

Relational Learning with Multiple Boosted
Detectors for Object Categorization
• Modeling relational dependencies between
different object categories
• Multiple detectors running in parallel
• Class label fixing based on confidence
• More accurate classification than AdaBoost
alone
• Higher recall than classic collective
classification
• Minimal decrease in recall for different classes
of objects

Object Classification Experiments

Identity-Based Authentication for
Cloud Computing
Hongwei Li, Yuanshun Dai, Ling Tian, and
Haomiao Yang
CloudCom ‘09

What did they do?
 Proposed identity-based authentication for cloud
computing, based on the identity-based hierarchical
model for cloud computing (IBHMCC) and
corresponding encryption and signature schemes
 Being certificate-free, the authentication protocol
aligned well with demands of cloud computing

Identity-Based Hierarchical Model for Cloud
Computing (IBHMCC)
 Define the identity of node is
the DN string from the root
node to the current node itself.
 The identity of entity N is
ID_N = DN_0 || DN_M || DN_N

Deployment of IBHMCC
 Root PKG setup and Low-level setup

Deployment of IBHMCC (cont.)
 After that, all nodes in the level-1 get and securely keep their secret keys and
the secret points.
 The public key and the Q-value are publicized.
 Then, Each node in the level-1 similarly repeats the above steps (2-5).

Identity-Based Encryption (cont.)

Identity-Based Authentication for Cloud
Computing
•Extends from TLS to handle
the IBE and IBS schemes

A Simple Technique for Securing
Data
at Rest Stored in a Computing
Cloud
Jeff Sedayao, Steven Su, Xiaohao Ma, Minghao
Jiang, and Kai Miao
CloudCom ’09

What did they do?
 Simple technique implemented with Open Source
software solves the confidentiality of data stored on
Cloud Computing Infrastructure by using public key
encryption to render stored data at rest unreadable
by unauthorized personnel, including system
administrators of the cloud computing service on
which the data is stored
 Validated their approach on a network measurement
system implemented on PlanetLab
 Used it on a service where confidentiality is critical –
a scanning application that validates external firewall
implementations

Problem Scope
 Goal is to ensure the confidentiality of data at rest
 “Data at rest” means that the data that is stored in a
readable form on a Cloud Computing service, whether
in a storage product like S3 or in a virtual machine
instance as in EC2

Problem Scope (cont.)
 To protect data at rest, they want to prevent other
users in the cloud infrastructure who might have
access to the same storage from reading the data
our process has stored
 They also want to prevent system administrators who
run the cloud computing service from reading the
data.
 They assume that it is unlikely for an adversary to
snoop on the contents of memory.
 If the adversary had that capability, it is unlikely
that we could trust the confidentiality of any of
the data that we generated there.

Problem Scope (cont.)
 While the administrative staff of the cloud
computing service could theoretically monitor the
data moving in memory before it is stored in disk, we
believe that administrative and legal controls should
prevent this from happening.
 They also do not guard against the modification of
the data at rest, although we are likely to be able to
detect this.

Solution Design (cont.)
 On a trusted host, collect the encrypted data, as
shown in Figure 3, and decrypt it with the collection
agent’s private key which stays on that host. Note
that in this case, we are in exclusive control of the
private key, which the cloud service provider has no
view or control over.
 They will discuss this feature of our solution later.

Implementation Experiences (cont.)

Privacy in a Semantic Cloud:
What’s Trust Got to Do with It?
Åsmund Ahlmann Nyre and Martin Gilje Jaatun
CloudCom’09

What did they do?
• A brief survey on recent work on privacy and trust
for the semantic web, and sketch a middleware
solution for privacy protection that leverages
probabilistic methods for automated trust and
privacy management for the semantic web

Trust Management
• Definition of trust
• The willingness of a party to be vulnerable to the
actions of another party based on the expectation
that the other will perform a particular action
important to the trustor, irrespective of the
ability to monitor and control that other party.

Trust Management (cont.)
• Trust Models
• Mayer, R., Davis, J., Schoorman, F.: An integrative
model of organizational trust. Academy of
Management Review
• The main factors of trustworthiness were identified as
ability, benevolence and integrity.
• On the trustor’s part, disposition to trust and perceived
risk were identified as the most influential factors with
regards to trust.
• Furthermore, the outcome of a trust relation
(experience) is assumed to influence one or more of the
trustworthiness factors and hence the trustworthiness
of the trustee.

• Trust Models
• The complexity of several proposed models does
not necessarily give better trust assessments
• Conrad, M., French, T., Huang, W., Maple, C.: A
lightweight model of trust propagation in a multi-
client network environment: to what extent does
experience matter?
• Proposed a lightweight model for trust propagation. The
parameters self confidence, experience, hearsay and
prejudice are used to model and assess trust. This
computational model also allows agents to compute a trust
value to automatically perform trust decisions.

• Trust Models
• Gil, Y., Artz, D.: Towards content trust of web
resources
• The idea is to arrive at content trust, where the
information itself is used for trust calculation.
• This allows for a whole new range of parameters (such as
bias, criticality, appearance, etc.) to be used when
assessing trust in resources.
• The problem of such parameters is that they require user
input, which conflicts with the assumption of agents
conducting the assessment autonomously.

• Trust Propagation
• Golbeck, J., Hendler, J.: Accuracy of metrics for
inferring trust and reputation in semantic web-
based social networks
• Inferring trust and reputation in social networks when
entities are not connected directly by a trust
relationship.
• Done by computing the weighted distance from the
source to the sink.
• Any distrusted entity is not included in the computation
since the trust assessments done by such entities are
worthless.

• Guha, R., Kumar, R., Raghavan, P., Tomkins, A.:
Propagation of trust and distrust
• Introduce the notion of distrust to address the problem
of expressing explicit distrust as a contrast to the
absence of trust.
• Absence of trust may come from lack of information to
conduct a proper trust assessment, while distrust
expresses that a proper assessment have been conducted
and that the entity should not be trusted.
• Furthermore, they argue that distrust could also be
propagated and proposes several propagation models in
addition to trust transitivity, including co-citation, which
is extensively used for web searches.

• Huang, J., Fox, M.S.: An ontology of trust: formal
semantics and transitivity
• claim that not all kinds of trust can be assumed to be
transitive.
• They note that trust based on performance, i.e. an entity
performing as expected repeatedly, is not necessarily
transitive, while trust based on a belief that the entity
will perform as expected often is.

Probabilistic Privacy Policy Enforcement
• A probabilistic approach to policy enforcement, where
users are given a probability that their requirements
will be respected and polices enforced.
• Thus when interacting with websites who are known
to be less trustworthy, policy adherence is given by a
probability metric that the website will actually
enforce its own policies.
• This enforcement model does not include a privacy or
trust model
• i.e. it is only occupied with how to handle uncertainty in
enforcement and provide a tool for interacting with non-
conforming entities while minimising the risks involved.

Probabilistic Privacy Policy Enforcement (cont.)

• Personal Data Recorder (PDR)
• Protecting users from this kind of aggregation
requires complete control of what information has
been distributed and to whom.
• Records what data is transmitted to which
receivers.
• Example: Consider the situation where a user wanting to
stay unidentified has provided his postal code and
anonymous e-mail address to a website. Later he also
provides age and given name (not the full name) and the
anonymous e-mail address. Now, the website is able to
combine the data (postal code, age and given name) to
identify the anonymous user
• The second interaction with the website should have
been blocked, since it enables the website to reveal
the user’s identity. The PDR allows the user to view

• Personal Data Monitor (PDM)
• Computing and assessing policies and behaviour,
and to update the personal data recorder with
inferred knowledge.
• Determine the likelihood that the personal
information distributed to the receiver will also
reach other.
• Example: sending an e-mail with a business proposition to
a specific employee of a company, it is likely that other
employees in that company also will receive the e-mail
(e.g. his superior).
• PDM is responsible for inferring other recipients and to
include such information in the Personal Information
Base.
• Hence, any interaction later on should consider this

• Trust Assessment Engine (TAE)
• Calculating trust values of different entities in
order to determine their trustworthiness.
• The TAE is focused solely on assessing
communicating parties and does not take into
account risk willingness, vulnerability and
criticality.

• Trust Monitor (TM)
• Detecting events that might affect the perceived
trustworthiness and the willingness to take risks.
• Calculating and deciding on what is an acceptable
trust level, given the circumstances.
• Any computed trust value and feedback received
from cooperating entities is stored in the trust
assessment repository

• Policy Decision Point (PDP)
• The final decision on whether to engage in
information exchange and if so; under what
conditions.
• Collects the views of both the TM and the PDM
and compares their calculations to the policies and
requirements found in the policy repository.
• The decision is reported back to the TM and PDM
to allow recalculation in case the decision alters
the calculated trust values or distribution of
personal information

Towards an Approach of Semantic
Access Control for Cloud Computing
Luokai Hu, Shi Ying, Xiangyang Jia, and Kai Zhao
CloudCom’09

What did they do?
• Analysis existing access control methods and present
a new Semantic Access Control Policy Language
(SACPL) for describing Access Control Policies (ACPs)
in cloud computing environment.
• Access Control Oriented Ontology System (ACOOS)
is designed as the semantic basis of SACPL.
• Ontology-based SACPL language can effectively solve
the interoperability issue of distributed ACPs.

Access Control Oriented Ontology System (ACOOS)
• Provide the common understandable semantic basis
for access control in cloud computing environments.
• Divided into four parts, Subject Ontology, Object
Ontology, Action Ontology and Attribute Ontology
• Web Ontology Language (OWL) is selected as the
modeling language of ACOOS.
• Ontology is helpful to construct authorization
policy within the scope of whole cloud computing
environment based on policy definition elements
with determined semantics.

• Subject Ontology
• Subject is the entity that has a number of action
permissions over object.
• e.g., a user, a user group, an organization, a role, a
process, a service
• Attribute of a subject is described by the data
property
• The role in subject ontology represents the
capability of a subject to implement a task.
• Access permission of resources can be
encapsulated in the role.
• If a subject is assigned to a role, it can access the
resources indirectly.

• Object Ontology
• Object is the entity as receptor of action and is
need for protection.
• e.g., data, documents, services and other resources.
• Attribute of an object is described by the data
property and object property of OWL with
hasObjectDataAttribute and hasObjectAttribute
respectively.
• Object group can also be used to define the rule to
organize objects.
• Each object group in fact establishes a new object
concept, all object individuals of the object concept have
object attribute values of the object group.

• Action Ontology
• With the cloud computing technology, usually a
large number of subjects and objects but only a
relatively small number of actions could be found
• e.g., such as reading, writing and execution
• Action also has properties, known as the
ActionAttribute, which describes various
information of action for authorization and
management.
• Action group can be defined with helpful for the
definition of rules.
• The definition of action group, nearly the same with the
object group, will not repeat it again.

• Attribute Ontology
• Attribute types are defined in the attribute
ontology, can be used to define the attribute of
almost all entities, including the subject, object
and action.
• The attribute value of entities is often needed to
determine whether meet the Permit conditions or
Deny ones.

Semantic Access Control Policy Language (SACPL)
• Policy markup language, such as XACML, supports
description and management of distributed policies.
• The ACP of an object (resource) may be completed by
a number of departments even organizations, such as
information systems department, human resources
and financial department.
• The same ACP may be applied to the internal network
protection, e-mail system, remote access systems, or
a cloud computing platform.
• As a result, in cloud computing environment, the issue
of interoperability among policies is more important
than ever before.

References
1. NIST (Authors: P. Mell and T. Grance), "The NIST Definition of Cloud Computing (ver. 15)," National Institute of Standards
and Technology, Information Technology Laboratory (October 7 2009).
2. J. McDermott, (2009) "Security Requirements for Virtualization in Cloud Computing," presented at the ACSAC Cloud
Security Workshop, Honolulu, Hawaii, USA, 2009.
3. J. Camp. (2001), “Trust and Risk in Internet Commerce,” MIT Press
4. T. Ristenpart et al. (2009) “Hey You Get Off My Cloud,” Proceedings of the 16th ACM conference on Computer and
communications security, Chicago, Illinois, USA
5. Security and Privacy in Cloud Computing, Dept. of CS at Johns Hopkins University.
www.cs.jhu.edu/~ragib/sp10/cs412
6. Cloud Security and Privacy: An Enterprise Perspective on Risks and Compliance by Tim Mather and Subra Kumaraswamy
7. Afraid of outside cloud attacks? You're missing the real threat. http://www.infoworld.com/d/cloud-computing/afraid-
outside-cloud-attacks-youre-missing-real-threat-894
8. Amazon downplays report highlighting vulnerabilities in its cloud service.
http://www.computerworld.com/s/article/9140074/Amazon_downplays_report_highlighting_vulnerabilities_in_its_cloud_s
ervice
9. Targeted Attacks Possible in the Cloud, Researchers Warn.
http://www.cio.com/article/506136/Targeted_Attacks_Possible_in_the_Cloud_Researchers_Warn
10. Vulnerability Seen in Amazon's Cloud-Computing by David Talbot.
http://www.cs.sunysb.edu/~sion/research/sion2009mitTR.pdf
11. Cloud Computing Security Considerations by Roger Halbheer and Doug Cavit. January 2010.
http://blogs.technet.com/b/rhalbheer/archive/2010/01/30/cloud-security-paper-looking-for-feedback.aspx
12. Security in Cloud Computing Overview.http://www.halbheer.info/security/2010/01/30/cloud-security-paper-looking-
for-feedback
13. Hey, You, Get Off of My Cloud: Exploring Information Leakage in Third-Party Compute Clouds by T. Ristenpart, E.
Tromer, H. Shacham and Stefan Savage. CCS’09
14. Cloud Computing Security. http://www.exforsys.com/tutorials/cloud-computing/cloud-computing-security.html
15. Update From Amazon Regarding Friday’s S3 Downtime by Allen Stern. Feb. 16, 2008.
http://www.centernetworks.com/amazon-s3-downtime-update
16. R. Ranchal, B. Bhargava, L.B. Othmane, L. Lilien, A. Kim, M. Kang, “Protection of Identity Information in Cloud Computing
without Trusted Third Party,“ Third International Workshop on Dependable Network Computing and Mobile Systems
(DNCMS) in conjunction with 29th IEEE Symposium on Reliable Distributed System (SRDS) 2010
17. P. Angin, B. Bhargava, R. Ranchal, N. Singh, L. Lilien, L.B. Othmane, “A User-Centric Approach for Privacy and Identity
Management in Cloud Computing,” 29th IEEE Symposium on Reliable Distributed System (SRDS) 2010
18. H. Khandelwal, et al., "Cloud Monitoring Framework,” Purdue University. Dec 2010.

Other References for Cloud Security
• M. Armbrust, et al., "Above the Clouds: A Berkeley View of Cloud Computing," UC Berkeley
Reliable Adaptive Distributed Systems LaboratoryFebruary 10 2009.
• Cloud Security Alliance, "Security Guidance for Critical Areas of Focus in Cloud Computing,
ver. 2.1," 2009.
• M. Jensen, et al., "On Technical Security Issues in Cloud Computing," presented at the 2009
IEEE International Conference on Cloud Computing, Bangalore, India 2009.
• P. Mell and T. Grance, "Effectively and Securely Using the Cloud Computing Paradigm," ed:
National Institute of Standards and Technology, Information Technology Laboratory, 2009.
• N. Santos, et al., "Towards Trusted Cloud Computing," in Usenix 09 Hot Cloud Workshop,
San Diego, CA, 2009.
• R. G. Lennon, et al., "Best practices in cloud computing: designing for the cloud," presented
at the Proceeding of the 24th ACM SIGPLAN conference companion on Object oriented
programming systems languages and applications, Orlando, Florida, USA, 2009.
• P. Mell and T. Grance, "The NIST Definition of Cloud Computing (ver. 15)," National
Institute of Standards and Technology, Information Technology LaboratoryOctober 7 2009.
• C. Cachin, et al., "Trusting the cloud," SIGACT News, vol. 40, pp. 81-86, 2009.
• J. Heiser and M. Nicolett, "Assessing the Security Risks of Cloud Computing," Gartner 2008.
• A. Joch. (2009, June 18) Cloud Computing: Is It Secure Enough? Federal Computer Week.
• AWS Amazon EC2: http://aws.amazon.com/ec2/
• Amazon CloudWatch: http://aws.amazon.com/cloudwatch/
• Iperf: http://iperf.sourceforge.net/

Cloud Blind References
• L. Ran, A. Helal, and S. Moore, “Drishti: An Integrated Indoor/Outdoor Blind Navigation System
and Service,” 2nd IEEE Pervasive Computing Conference (PerCom 04).
• S.Willis, and A. Helal, “RFID Information Grid and Wearable Computing Solution to the Problem
of Wayfinding for the Blind User in a Campus Environment,” IEEE International Symposium on
Wearable Computers (ISWC 05).
• Y. Sonnenblick. “An Indoor Navigation System for Blind Individuals,” Proceedings of the 13th
Annual Conference on Technology and Persons with Disabilities, 1998.
• J. Wilson, B. N. Walker, J. Lindsay, C. Cambias, F. Dellaert. “SWAN: System for Wearable Audio
Navigation,” 11th IEEE International Symposium on Wearable Computers, 2007.
• J. Nicholson, V. Kulyukin, D. Coster, “ShopTalk: Independent Blind Shopping Through Verbal
Route Directions and Barcode Scans,” The Open Rehabilitation Journal, vol. 2, 2009, pp. 11-23.
• Bach-y-Rita, P., M.E. Tyler and K.A. Kaczmarek. “Seeing with the Brain,” International Journal of
Human-Computer Interaction, vol 15, issue 2, 2003, pp 285-295.
• Y.K. Kim, K.W. Kim, and X.Yang, “Real Time Traffic Light Recognition System for Color Vision
Deficiencies,” IEEE International Conference on Mechatronics and Automation (ICMA 07).
• R. Charette, and F. Nashashibi, “Real Time Visual Traffic Lights Recognition Based on Spot Light
Detection and Adaptive Traffic Lights Templates,” World Congress and Exhibition on Intelligent
Transport Systems and Services (ITS 09).
• A.Ess, B. Leibe, K. Schindler, and L. van Gool, “Moving Obstacle Detection in Highly Dynamic
Scenes,” IEEE International Conference on Robotics and Automation (ICRA 09).
• P. Angin, B. Bhargava, R. Ranchal, N. Singh, L. Lilien, L. B. Othmane, “A User-centric Approach
for Privacy and Identity Management in Cloud Computing,” submitted to SRDS 2010.

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