Big data security and privacy issues in the

International Journal of Network Security & Its Applications (IJNSA) Vol.8, No.1, January 2016
DOI : 10.5121/ijnsa.2016.8104 59
BIG DATA SECURITY AND PRIVACY ISSUES IN THE
CLOUD
Ali Gholami and Erwin Laure
High Performance Computing and Visualization Department,
KTH- Royal Institute of Technology, Stockholm, Sweden
ABSTRACT
Many organizations demand efficient solutions to store and analyze huge amount of information. Cloud
computing as an enabler provides scalable resources and significant economic benefits in the form of
reduced operational costs. This paradigm raises a broad range of security and privacy issues that must be
taken into consideration. Multi-tenancy, loss of control, and trust are key challenges in cloud computing
environments. This paper reviews the existing technologies and a wide array of both earlier and state-of-
the-art projects on cloud security and privacy. We categorize the existing research according to the cloud
reference architecture orchestration, resource control, physical resource, and cloud service management
layers, in addition to reviewing the recent developments for enhancing the Apache Hadoop security as one
of the most deployed big data infrastructures. We also outline the frontier research on privacy-preserving
data-intensive applications in cloud computing such as privacy threat modeling and privacy enhancing
solutions.
KEYWORDS
Cloud Security, Privacy, Trust, Big Data, Virtualization, Data Protection
1. INTRODUCTION
During recent years, data production rate has been growing exponentially [1, 11]. Many
organizations demand efficient solutions to store and analyze these big amount data that are
preliminary generated from various sources such as high throughput instruments, sensors or
connected devices. For this purpose, big data technologies can utilize cloud computing to provide
significant benefits, such as the availability of automated tools to assemble, connect, configure
and reconfigure virtualized resources on demand. These make it much easier to meet
organizational goals as organizations can easily deploy cloud services.
This shift in paradigm that accompanies the adoption of cloud computing is increasingly giving
rise to security and privacy considerations relating to facets of cloud computing such as multi-
tenancy, trust, loss of control and accountability [69]. Consequently, cloud platforms that handle
big data that contain sensitive information are required to deploy technical measures and
organizational safeguards to avoid data protection breakdowns that might result in enormous and
costly damages.
Sensitive information in the context of cloud computing encompasses data from a wide range of
different areas and disciplines. Data concerning health is a typical example of the type of
sensitive information handled in cloud computing environments, and it is obvious that most
individuals will want information related to their health to be secure. Hence, with the proliferation
of these new cloud technologies in recent times, privacy and data protection requirements have
been evolving to protect individuals against surveillance and database disclosure. Some examples
of such protective legislation are the EU Data Protection Directive (DPD) [2] and the US Health
Insurance Portability and Accountability Act (HIPAA) [3], both of which demand privacy
preservation for handling personally identifiable information.

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This paper presents an overview of the research on security and privacy of big sensitive data in
cloud computing environments. We identify new developments in the areas of orchestration,
resource control, physical hardware, and cloud service management layers of a cloud provider.
We also review the state-of-the-art for the Apache Hadoop security, in addition to outlining
privacy-preserving sensitive data processing approaches for handling big data in cloud computing
such as privacy threat modeling and privacy enhancing solutions.
The rest of this paper is organized as follows. Section 2 gives an overview of big data, cloud
computing concepts and technologies. Section 3 describes the security and privacy issues that
need to be solved in order to provide secure data management for cloud environments. Section 4,
reviews the existing security solutions that are being used in the area of cloud computing. Section
5 describes research on privacy-preserving solutions for big sensitive data. Finally, in Section 6,
we present our findings and conclusions.
2. KEY CONCEPTS AND TECHNOLOGIES
While it is practical and cost effective to use cloud computing for data-intensive applications,
there can be issues with security when using systems that are not provided in-house. To look into
these and find appropriate solutions, there are several key concepts and technologies that are
widely used in data-intensive clouds that need to be understood, such as big data infrastructures,
virtualization mechanisms, varieties of cloud services, and “container” technologies.
2.1. Big Data
Computers produce soaring rates of data that is primarily generated by Internet of Things (IoT),
Next-Generation Sequencing (NGS) machines, scientific simulations and other sources of data
which demand efficient architectures for handling the new datasets. In order to cope with this
huge amount of information, “Big Data” solutions such as the Google File System (GFS) [9],
Map/Reduce (MR), Apache Hadoop and the Hadoop Distributed File System (HDFS) have been
proposed both as commercial or open-source.
Key vendors in the IT industry such as IBM, Oracle, Microsoft, HP, Cisco and SAP have
preliminary customized these big data solutions. There were different definitions and hype around
“Big Data” at the emerging points. During the past few years, NIST formed the big data working
group1
as a community with joint members from industry, academia and government with the aim
of developing a consensus definition, taxonomies, secure reference architectures, and technology
roadmap. It identifies big data characteristics as extensive datasets that are diverse, including
structured, semi-structured, and unstructured data from different domains (variety); large orders
of magnitude (volume); arriving with fast rate (velocity); change in other characteristics
(variability) [72].
Table 1 summarizes the big data technologies from batch processing in 2000 to present with most
significant stages and products.
1
http://bigdatawg.nist.gov/home.php

61
Table 1, Evolution of the Big Data from batch to real-time analytics processing [70]
Stage/Year Characteristics Examples
Batch
Processing
2003-2008
Big amount of data is collected, entered, processed and
then the batch results are produced. Distributed file
systems (DFS) are used for fault-tolerant and scalability.
Parallel programming models such as MR are used for
efficient processing of data.
GFS, MR,
HDFS,
Apache Hadoop
Ad-hoc
(NoSQL)
2005 - 2010
Support random read/write access to overcome
shortcomings of DFS that are appropriate for sequential
data access. NoSQL databases solve this issue by
offering column based or key-value stores, in addition to
support for storage of large unstructured datasets such as
documents or graphs.
CoachDB, Redis,
Amazon DynamoDB,
Google Big Table,
HBase, Cassandra,
MongoDB
SQL-like
2008 - 2010
Simple programming interfaces to query and access the
datastores. This approach provides functionalities similar
to the traditional data warehousing mechanisms.
Apache Hive/Pig,
PrestoDB, HStore,
Google Planner
Stream
Processing
2010 - 2013
Data are pushed continuously as streams to servers for
processing before storing them. Streaming data usually
have unpredictable incoming patterns. Such data streams
are processed using fast, fault-tolerant, and high
availability solutions.
Hadoop Streaming,
Google Big Query,
Google Dremel,
Apache Drill, Samza
Apache Flume/Hbase,
Apache Kafka/Storm
Real-time
Analytical
Processing
2010 - 2015
Automated decision making for streams that are generated
from the machine-to-machine applications or other live
channels. This architecture helps to apply real-times rules
for the incoming events and existing events within a
domain.
Apache Spark,
Amazon Kinesis,
Google Dataflow
Big data analytics can benefit enterprises and organizations by solving many problems in
manufacturing, education, telecommunication, insurance, government, energy, retail,
transportation, and healthcare [70].
Over the past few years, major IT vendors (such as Amazon, Microsoft and Google) have
provided virtual machines (VMs), via their clouds, that customers could rent. These clouds utilize
hardware resources and support live migration of VMs in addition to dynamic load-balancing and
on-demand provisioning. This means that, by renting VMs via a cloud, the entire datacenter
footprint of a modern enterprise can be reduced from thousands of physical servers to a few
hundred (or even just dozens) of hosts.
2.2. Virtualization Mechanisms
A hypervisor or virtual machine monitor (VMM) is a key component that resides between VMs
and hardware to control the virtualized resource [4]. It provides the means to run several isolated
virtual machines on the same physical host. Hypervisors can be categorized into two groups [5] as
follows.
● Type I: Here the hypervisor runs directly on the real system hardware, and there is no
operating system (OS) under it. This approach is efficient as it eliminates any
intermediary layers. Another benefit with this type of hypervisor is that security levels

62
can be improved by isolating the guest VMs. That way, if a VM is compromised, it can
only affect itself and will not interfere with the hypervisor or other guest VMs.
● Type II: The second type of hypervisor runs on a hosted OS that provides virtualization
services, such as input/output (IO) device support and memory management. All VM
interactions, such as IO requests, network operations and interrupts, are handled by the
hypervisor.
Xen2
and kernel virtual machine (KVM)3
are two popular open-source hypervisors (respectively
of Type I and Type II). Xen runs directly on the underlying hardware and it inserts a virtualization
layer between the system hardware and the virtual machines. The OSs running in the VMs
interact with the virtual resources as if they were actually physical resources. KVM is a
virtualization feature in the Linux Kernel that makes it possible to safely execute guest code
directly on the host CPU.
2.3. Cloud Computing Characteristics
When considering cloud computing, we need to be aware of the types of services that are offered,
the way those services are delivered to those using the services, and the different types of people
and groups that are involved with cloud services.
Cloud computing delivers computing software, platforms and infrastructures as services based on
pay-as-you go models. Cloud service models can be deployed for on-demand storage and
computing power in various ways: Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS)
and Infrastructure-as-a-Service (IaaS). Cloud computing service models have been evolved
during the past few years within a variety of domains using the “as-a-Service” concept of cloud
computing such as Business Integration-as-a-Service, Cloud-Based Analytics-as-a-Service
(CLAaaS), Data-as-a-Service (DaaS) [61, 62]. This paper refers to the NIST cloud service
models features [6] that are summarized in Table 2 that can be delivered to consumers using
different models such as a private cloud, community cloud, public cloud, or hybrid cloud.
Table 2, Categorization of Cloud Service Models and Features
Service Model Function Example
SaaS Allows consumers to run applications by
virtualizing hardware on cloud providers
Salesforce Customer
Relationship Management
(CRM)4
PaaS Provides the capability to deploy custom
applications with their dependencies within an
environment called a container
Google App Engine5
,
Heroku
6
IaaS Provides a hardware platform as a service such
as virtual machines, processing, storage,
networks and database services
Amazon Elastic Compute
Cloud (EC2)
7
2
Xen hypervisor, http://xen.org/products/xenhyp.html
3
KVM, http://www.linux-kvm.org/
4
Salesforce CRM, https://www.salesforce.com/crm/
5
Google App Engine, https://appengine.google.com
6
Heroku, https://www.heroku.com
7
Amazon EC2, https://aws.amazon.com/ec2/

63
The NIST cloud computing reference architecture [7], defines five major actors in the cloud
arena: cloud consumers, cloud providers, cloud carriers, cloud auditors and cloud brokers. Each
of these actors is an entity (either a person or an organization) that participates in a cloud
computing transaction or process, and/or performs cloud computing tasks.
A cloud consumer is a person or organization that uses services from cloud providers in the
context of a business relationship. A cloud provider is an entity makes cloud services available to
interested users. A cloud auditor conducts independent assessments of cloud services, operations,
performance and security in relation to the cloud deployment. A cloud broker is an entity that
manages the use, performance and delivery of cloud services, and also establishes relationships
between cloud providers and cloud consumers. A cloud carrier is an entity that provides
connectivity and transport of cloud services from cloud providers to cloud consumers through the
physical networks.
The activities of cloud providers can be divided into five main categories: service deployment,
resource abstraction, physical resources, service management, security and privacy [7]. Service
deployment consists of delivering services to cloud consumers according to one of the service
models (SaaS, PaaS, IasS). Resource abstraction refers to providing interfaces for interacting with
networking, storage and compute resources. The physical resources layer includes the physical
hardware and facilities that are accessible via the resource abstraction layer. Service management
includes providing business support, resource provisioning, configuration management,
portability and interoperability to other cloud providers or brokers. The security and privacy
responsibilities of cloud providers include integrating solutions to ensure legitimate delivery of
cloud services to the cloud consumers. The security and privacy features that are necessary for the
activities of cloud providers are described in Table 3 [10].
Table 3, Security and Privacy Factors of the Cloud Providers
Security Context Description
Authentication
and
Authorization
Authentication and authorization of cloud consumers using
pre-defined identification schemes
Identity
and
Access Management
Cloud consumer provisioning and deprovisioning via
heterogeneous cloud service providers
Confidentiality,
Integrity, Availability
(CIA)
Assuring the confidentiality of the data objects, authorizing
data modifications and ensuring that resources are available
when needed
Monitoring
and
Incident Response
Continuous monitoring of the cloud infrastructure to ensure
compliance with consumer security policies and auditing
requirements
Policy
Management
Defining and enforcing rules to enforce certain actions such as
auditing and proof of compliance
Privacy
Protect personally identifiable information (PII) within the
cloud from adversarial attacks that aim to find out the identity
of the person that the PII relates to

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The majority of cloud computing infrastructures consist of reliable services delivered through
data centers to achieve high availability through redundancy. A data center or computer center is
a facility used to house computer systems and associated components, such as storage and
network systems. It generally includes redundant or backup power units, redundant network
connections, air conditioning, and fire safety controls.
2.4. Container Technology
Clouds based on Linux container (LXC)8
technology are considered to be next-generation clouds,
so LXCs has become an important part of the cloud computing infrastructures because of their
ability to run several OS-level isolated VMs within a host with a very low overhead. LXCs are
built on modern kernel features. An LXC resembles a light-weight execution environment within
a host system that runs instructions native to the core CPU while eliminating the need for
instruction level emulation or just-in-time compilation [8]. LXCs contain applications,
configurations and the required storage dependencies, in a manner similar to the just enough OS
(JeOS). Containers are built on the hardware and OS but they make use of kernel features called
chroots, cgroups and namespaces to construct a contained environment without the need for a
hypervisor. The most recent container technologies are Solaris Zones, OpenVZ and LXC.
Docker9
is another container management tool – it was introduced in 2013 and is based on
namespaces, cgroups and SELinux. Docker provides automation for the deployment of containers
through remote APIs and has additional features that make it possible to create standardized
environments for developing applications. This has made Docker a popular technology. Creating
the standardized environments is achieved using a layered image format that enables users to add
or remove applications and their dependencies to form a trusted image. Docker adds portable
deployment of LXCs across different machines. In cloud terms, one can think of LXC as the
hypervisor and Docker as both the open virtualization appliance and the provision engine [12].
Docker images can run unchanged on any platform that supports Docker. In Docker, containers
can be created from build files such as Web service management.
The use of containers in cloud computing is increasingly becoming popular amongst cloud
providers such as Google and Microsoft. Significant improvements in performance and security
are the main driving factors for employing containers compared to virtualization using
hypervisors in cloud infrastructures.
3. SECURITY AND PRIVACY CHALLENGES
Cloud computing has raised several security threats such as data breaches, data loss, denial of
service, and malicious insiders that have been extensively studied in [67, 68]. These threats
mainly originate from issues such as multi-tenancy, loss of control over data and trust.
(Explanations of these issues follow in the next subsection.)
Consequently, the majority of cloud providers – including Amazon’s Simple Storage Service
(S3)10
, the Google Compute Engine11
and the Citrix Cloud Platform12
- do not guarantee specific
levels of security and privacy in their service level agreements (SLAs) as part of the contractual
terms and conditions between cloud providers and consumers. This means that there are
important concerns related to security and privacy that must be taken into consideration in using
8
LXC Containers, https://linuxcontainers.org/
9
Docker Containers, https://www.docker.com/
10
Amazon S3 SLA, https://aws.amazon.com/s3/sla/
11
Google Compute Engine SLA, https://cloud.google.com/compute/sla
12
Citrix Cloud Platform SLA, https://www.citrix.se/products/cloudplatform/overview.html

65
cloud computing by all parties involved in the cloud computing arena. These are discussed in the
subsection 2.2.
3.1. Security Issues in Cloud Computing
 Multi-tenancy: Multi-tenancy refers to sharing physical devices and virtualized
resources between multiple independent users. Using this kind of arrangement means that
an attacker could be on the same physical machine as the target. Cloud providers use
multi-tenancy features to build infrastructures that can efficiently scale to meet
customers’ needs, however the sharing of resources means that it can be easier for an
attacker to gain access to the target’s data.
 Loss of Control: Loss of control is another potential breach of security that can occur
where consumers’ data, applications, and resources are hosted at the cloud provider’s
owned premises. As the users do not have explicit control over their data, this makes it
possible for cloud providers to perform data mining over the users’ data, which can lead
to security issues. In addition, when the cloud providers backup data at different data
centers, the consumers cannot be sure that their data is completely erased everywhere
when they delete their data. This has the potential to lead to misuse of the unerased data.
In these types of situations where the consumers lose control over their data, they see the
cloud provider as a black-box where they cannot directly monitor the resources
transparently.
 Trust Chain in Clouds: Trust plays an important role in attracting more consumers by
assuring on cloud providers. Due to loss of control (as discussed earlier), cloud users rely
on the cloud providers using trust mechanisms as an alternative to giving users
transparent control over their data and cloud resources. Therefore, cloud providers build
confidence amongst their customers by assuring them that the provider's operations are
certified in compliance with organizational safeguards and standards.
3.2. Privacy Considerations of Processing Sensitive Data
The security issues in cloud computing lead to a number of privacy concerns. Privacy is a
complex topic that has different interpretations depending on contexts, cultures and communities,
and it has been recognized as a fundamental human right by the United Nations [13]. It worth
nothing that privacy and security are two distinct topics although security is generally necessary
for providing privacy [59, 69].
Several efforts have been made to conceptualize privacy by jurists, philosophers, researchers,
psychologists, and sociologists in order to give us a better understanding of privacy – for
example, Alan Westin’s research in 1960 is considered to be the first significant work on the
problem of consumer data privacy and data protection. Westin [14] defined privacy as follow.
“Privacy is the claim of individuals, groups, or institutions to determine for
themselves when, how, and to what extent information about them is
communicated to others.”
The International Association of Privacy Professionals (IAPP)13
glossary 27 refers to privacy as
the appropriate use of information under the circumstances. The notion of what constitutes
appropriate handling of data handling varies depending on several factors such as individual
preferences, the context of the situation, law, collection, how the data would be used and what
information would be disclosed.
13
IAPP Glossary, https://iapp.org/resources/glossary

66
In jurisdictions such as the US, “privacy” is the term that is used to encompass the relevant laws,
policies and regulations, while in the EU the term “data protection” is more commonly used when
referring to privacy laws and regulations. Legislation that aims to protect the privacy of
individuals – such as the European Union (EU) DPD [2], the Gramm-Leach-Bliley Act (GLBA)
[15], the Right to Financial Privacy Act (RFPA) [16], the Telecommunications Act of 1996 [71],
and the HIPAA [3] – can become very complicated and have a variety of specific requirements.
Organizations collecting and storing data in clouds that are subject to data protection regulations
must ensure that the privacy of the data is preserved appropriately to lay the foundations for legal
access to sensitive personal data.
The development of a legal definition for cybercrime, the issue of jurisdiction (who is responsible
for what information and where are they held responsible for it) and the regulation of data
transfers to third countries [17] are among other challenging issues when it comes to security in
cloud computing. For example, the DPD, which is the EU’s initial attempt at privacy protection,
presents 72 recitals and 34 articles to harmonize the regulations for information flow within the
EU Member States.
The DPD highlights the demand for cross-border transfer of data through non-legislative
measures and self-control. One example of where these types of privacy principles are being used
is the Safe Harbor Agreement (SHA) which makes it possible transfer data to US-based cloud
providers that are assumed to have appropriate data protection mechanisms. However, cloud
carriers are not subject to the SHA, which leads to complexity in respect to international laws.
There is an ongoing effort [18] to replace the EU DPD with a new a data protection regulation
containing 91 articles that aims to lay out a data protection framework in Europe. The proposed
regulations expand the definition of personal data protection to cover any information related to
the people who are the subjects of the data, irrespective of whether the information is private,
public or professional in nature. The regulations also include definitions of new roles related to
handling data (such as data transfer officers) and propose restricting the transfer of data to third-
party countries that do not guarantee adequate levels of protection. Currently Argentina, Canada,
Guernsey, Jersey, the Isle of Man, Israel, Switzerland, the US Safe Harbor Privacy Program, and
the US Transfer of Air Passenger Name Data Record are considered to offer adequate protection.
The new regulations consider imposing significant penalties for privacy breaches that result from
violations of the regulations, for example, such a penalty could be 0.5 percent of the worldwide
annual turnover of the offending enterprise.
3.3. Big Data Security Challenges
Many data infrastructures have been deployed based on the Apache Hadoop without demand for
strong security [81]. Only few companies have deployed secure Hadoop environments such as
Yahoo!. Therefore, Hadoop built-in security requires tailoring for different security requirements.
Hadoop operates in two modes: normal (non-secure) and secure modes [66].
Hadoop Normal Mode configurations are in non-secure mode. The default mode has no
authentication enforcement. It relies on client-side libraries to send the credentials from the user
machine operating system in context of the protocol [81]. Clusters are usually deployed onto
private clouds with restricted access to authorized users.
In this model, all users and programmers have similar access rights to all data in HDFS. Any user
that submits a job could access any data in the cluster and reads any data belonging to other users.
Also MR framework does not authenticate or authorize submitted tasks. An adversary is able to
tamper with the priorities of other Hadoop jobs in order to make his job complete faster or
terminate other jobs [73].

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Data confidentiality and key management are also missing in the Hadoop default mode. There is
no encryption mechanism deployed to keep data confidential in HDFS and MR clusters. For
scenarios where confidentiality is a requirement other distribution of Hadoop that are discussed in
subsection 5.1 can be utilized.
Hadoop Secure Mode14
consist of authentication, service level authorization and authentication
for Web consoles. By configuring Hadoop in secure mode, each user and service require
authentication by Kerberos in order to use Hadoop services. Since Hadoop requires a user
identifier string to identify users, a POSIX-compliant username can be used for authentication
purposes. The usernames can also be used during authorization to check the access control lists
(ACL). Additionally, Hadoop supports the notion of POSIX groups to allow a group of users to
access HDFS resources. Authorization checks through ACLs and file permissions are still
performed against the client supplied user identifiers.
There is a remote procedure call (RPC) library that is used to provide clients secure access to
Hadoop services through sending username over simple authentication and security layer (SASL).
SASL is built on Kerberos or DIGEST-MD5. In Kerberos mode, users acquire a ticket for
authentication using SASL for mutual authentication. DIGEST-MD5 mechanism uses shared
symmetric keys for user authentication with servers to avoid overheads of using a key distribution
center (KDC) as a third party for authentication. RPC also provides data transmission
confidentiality between Hadoop services clients through encryption in contrast to the Web-
console that utilized HTTPS.
Kerberos can be used for user authentication in Hadoop secure deployments over encrypted
channels. For organizations that require other security solutions not involving Kerberos, this
demands setting up a separate authentication system. Hadoop implements SASL/GSSAPI for
mutual authentication of users with Kerberos, running processes, and Hadoop services on RPC
connections [73]. A secure deployment requires Kerberos settings where each service reads
authentication information saved in keytab file with appropriate permission. A keytab is a file that
contains pairs of Kerberos principals and encrypted keys. Keytabs are used by the Haoop services
to avoid entering password for authentication.
4. THE CLOUD SECURITY SOLUTIONS
This section reviews the research on security solution such as authentication, authorization, and
identity management that were identified in Table 3 [10] as being necessary so that the activities
of cloud providers are sufficiently secure.
4.1. Authentication and Authorization
In [19] the authors propose a credential classification and a framework for analyzing and
developing solutions for credential management that include strategies to evaluate the complexity
of cloud ecosystems. This study identifies a set of categories relevant for authentication and
authorization for the cloud focusing on infrastructural organization which include classifications
for credentials, and adapt those categories to the cloud context. The study also summarizes
important factors that need to be taken into consideration when adopting or developing a solution
for authentication and authorization – for example, identifying the appropriate requirements,
categories, services, deployment models, lifecycle, and entities. In other work, a design model for
multi-factor authentication in cloud computing environments is proposed in [20], and this model
includes an analysis of the potential security threats in the proposed model. Another
authentication solution is seen with MiLAMob [21], which provides a SaaS authentication
14
http://hadoop.apache.org/docs/current/hadoop-project-dist/hadoop-common/SecureMode.html

68
middleware for mobile consumers of IaaS cloud applications. MiLAMob is a middleware-layer
that handles the real-time authentication events on behalf of consumer devices with minimal
HTTP traffic. The middleware currently supports mobile consumption of data on IaaS clouds
such as Amazon’s S3.
FermiCloud [22] uses another approach for authentication and authorization - it utilizes public
key infrastructure (PKI) X.509 certificates for user identification and authentication. FermiCloud
is built in OpenNebula115
and it develops both X.509 authentication in Sunstone OpenNebula – a
Web interface intended for user management – and X.509 authentication via command-line
interfaces. To avoid the limitations of OpenNebula access control lists that are used for
authorization after successful authentication of users, authors integrated an existing local
credential mapping service. This solution has also been extended in cloud federations to authorize
users across different cloud providers that have established trust relationships through trusted
certification authorities.
Tang et al. [23] introduce collaborative access control properties such as centralized facilities,
agility, homogeneity, and outsourcing trust. They have introduced an authorization-as-a-service
(AaaS) approach using a formalized multi-tenancy authorization system, and providing
administrative control over enhanced fine-grained trust models. Integrating trust with
cryptographic role-based access control (RBAC) is another solution [24] that ensures trust for
secure sharing of data in the cloud. The authors propose using cryptographic RBAC to enforce
authorization policies regarding the trustworthiness of roles that are evaluated by the data owner.
Another feature of the authorization system in this solution is that it develops a new concept using
role inheritance for evaluating the trustworthiness of the system. In another study, Sendo et al.
[25] propose a user-centric approach for platform-level authorization of cloud services using the
OAuth2 protocol to allow services to act on behalf of users when interacting with other services
in order to avoid sharing usernames and passwords across service
4.2. Identity and Access Management
The important functionalities of identity management systems for the success of clouds in relation
to consumer satisfaction is discussed in [26]. The authors also present an authorization system for
cloud federation using Shibboleth - an open source implementation of the security assertion
markup language (SAML) for single sign-on with different cloud providers. This solution
demonstrates how organizations can outsource authentication and authorization to third-party
clouds using an identity management system. Stihler et al. [27] also propose an integral federated
identity management for cloud computing. A trust relationship between a given user and SaaS
domains is required so that SaaS users can access the application and resources that are provided.
In a PaaS domain, there is an interceptor that acts as a proxy to accept the user’s requests and
execute them. The interceptor interacts with the secure token service (STS), and requests the
security token using the WS-Trust specification.
Contrail [29] is another approach that aims to enhance integration among heterogeneous clouds
both vertically and horizontally. Vertical integration provides a unified platform for the different
kinds of resources while horizontal integration abstracts the interaction models of different cloud
providers. In [29] the authors develop a horizontal federation scheme as a requirement for vertical
integration. The proposed federation architecture contains several layers, such as users’ identities,
business logic and a federation manager to support APIs for resources, storage, and networking
across different providers.
15
http://opennebula.org/

69
Model-driven specification and enforcement of task-based entailment constraints in distributed
service-based business processes [30] is another effort, where the authors describe challenges of
secure sharing of data using RBAC. Identity and access management tasks are enforced using
Web services where SAML tokens are used for authentication users across various identity
providers.
E-ID authentication and uniform access to cloud storage service providers [31] is an effort to
build identity management systems for authenticating Portuguese citizens using national e-
identification cards for cloud storage systems. In this approach, the OAuth protocol is integrated
for authorizing the cloud users. The e-ID cards contain PKI certificates that are signed by several
levels of governmental departments. A certification authority is responsible for issuing the e-ID
cards and verifying them. The e-ID cards enable users for identity-based encryption of data in
cloud storage.
In [74], the authors consider the issues related to inter-cloud federation and the proposed
ICEMAN identity management architecture. ICEMAN discusses identity life cycle, self-service,
key management, provisioning and deprovisioning functionalities that need to be included in an
appropriate intercloud identity management system.
The EGI delivered a hybrid federated cloud [32] as a collaboration of communities developing,
innovating, operating and using clouds for research and education. The EGI federated cloud
provides IaaS, persistent block storage attached to VMs, and object-level storage for transparent
data sharing. The EGI controls access to resources using X.509 certificates and the concept of
“Virtual Organization” (VO). VO refers to a dynamic set of users or institutions using resource-
sharing rules and conditions. The authorization attributes are issued through a VO management
system that can be integrated with SAML for federation.
4.3. Confidentiality, Integrity, and Availability (CIA)
Santos et al. [33] extend the Terra [34] design that enables users to verify the integrity of VMs in
the cloud. The proposed solution is called the trusted cloud computing platform (TCCP), and the
whole IaaS is considered to be a single system instead of granular hosts in Terra. In this approach,
all nodes run a trusted virtual machine monitor to isolate and protect virtual machines. Users are
given access to cloud services through the cloud manager component. The external trusted entity
(ETE) is another component that provides a trust coordinator service in order to keep track of the
trusted VMs in a cluster. The ETE can be used to attest the security of the VMs. A TCCP
guarantees confidentiality and integrity in data and computation and it also enables users to attest
to the cloud service provider to ensure whether the services are secure prior to setting up their
VMs. These features are based on the trusted platform module (TPM) chip. The TPM contains a
private endorsement key that uniquely identifies the TPM and some cryptographic functions that
cannot be altered.
In 2011, Popa et al. proposed CloudProof [35] as a secure storage system to guarantee
confidentiality, integrity and write-serializability using verifiable proofs of violation by external
third parties. Confidentiality is ensured by private keys that are known only to the owner of the
data that is to be encrypted. The main idea behind CloudProof is the use of the attestation
mechanism. Attestations provide proof of sanity of users, data owners and cloud service
providers. Data owners use a block identifier to acquire the content of a block. This mechanism
enables users to store data by putting a block identifier and the contents of the block in the cloud.
The attestation structure implements a solution called “block hash” for performing integrity
checks through signature verification. The block hash provides proof for write-serializabilty using
a forked sequence of the attestations while a chain hash is used for a broken chain of attestations
which are not sequenced correctly.

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Fuzzy authorization (FA) for cloud storage [36] is another flexible and scalable approach to
enable data to be shared securely among cloud participants. FA ensures confidentiality, integrity
and secure access control by utilizing secret sharing schemes for users with smartphones who are
using the cloud services.
In [37] the authors define threats to cloud server hypervisors thorough analysis of the codebase of
two popular open-source hypervisors: Xen and KVM. In addition, they discuss the vulnerabilities
reports associated with them. As a result, a model is proposed for characterization of hypervisor
vulnerabilities in three dimensions: the trigger source, the attack vector and the attack target. The
attack vector consists of the Hypervisor functionality that makes security breaches possible - for
example, virtual CPUs, symmetric multiprocessing, soft memory management units, interrupt and
timer mechanisms, IO and networking, paravirtualized IO, VM exits, hypercalls, VM
management (configure, start, pause and stop VMs), remote management, and software
hypervisor add-ons. Successful exploitation of a vulnerability in these functionalities enables an
attacker to compromise the confidentiality, integrity, or availability of the Hypervisor or one of its
guest VMs.
The vulnerability reports in [37] show 59 vulnerability cases for Xen and 38 cases for KVM.
Approximately 50 percent of these vulnerabilities are the same for both Xen/KVM and consist of
issues relating to confidentiality, integrity and availability. The remote management software of
Xen contributes to 15.3 percent of the vulnerabilities that demonstrates the increase attack surface
by non-essential services. The VM management component contributes to 11.9 percent of the
vulnerabilities in Xen compared to 5.3 percent in KVM. The lower vulnerability rate in KVM is
due to the libvirt toolkit inside the hypervisor, whereas Xen’s decision to allocate an entire
privileged is done in Dom0. Other factors that have been studied in [20] are trigger sources and
likely attack targets, including the overall network, the guest VM’s user-space, the guest VM’s
kernel-space, the Dom0/host OS, and the hypervisor. The most common trigger source is the
guest VM user-space, which gives rise to 39.0 percent of Xen’s and 34.2 percent of KVM’s
vulnerabilities. This makes it possible for any user-space guest VM to be a threat to the
hypervisor. The guest VM kernel-space has around 32 percent of the total vulnerabilities in both
cases. The authors show Dom0 to be a more common target than the hypervisor in Xen, whereas
the host OS in KVM is a less common target compared to the hypervisor. The location of the IO
device emulation back-end drivers plays an important factor in this difference. The IO and
network device emulation functionalities cause one third of the 15 vulnerabilities in both.
In [38] the authors propose Swap and Play as a new approach for live updating of hypervisors
without the need to reboot the VM for high availability. The proposed design is scalable, usable
and applicable in cloud environments and it has been implemented in Xen as one of the most
popular hypervisors. Swap and Play provides methods to transfer the in-memory state of the
running hypervisor to the updating state, in addition to updating the underlying host. Swap and
Play consists of three independent phases: preparation, distribution and update. In the preparation
phase information for the later state transfer is collected. The distribution phase deploys the
update package on the target host for updating. In the last step, the update package is patched to
individual hosts in the cloud. Each host applies the update package independently of the others
and does not require any network resources. The Xen implementation of the Swap and Play
solution is called SwapVisor. SwapVisor introduces a new hypercall in the Xen architecture. A
hypercall is a trap from a domain to the hypervisor (similar to a syscall from an application to the
kernel). Hypercalls are used by domains to request privileged operations such as updating page
tables. The experiments show that updating from Xen version 4.2.0 to version 4.2.1 is fulfilled
within approximately 45 ms which seems to be intangible and have almost zero effect on the
network performance.

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Klein et al. [39] improve cloud service resilience using a load-balancing mechanism called
brownout. The idea behind this solution is to maximize the optional contents to provide a solution
that is resilient to volatility in terms of flash crowds and capacity shortages (through load-
balancing over replicas) when compared to other approaches that are implemented using
response-time or queue length. In another effort [40] the authors proposed a synchronization
mechanism for cloud accounting systems that are distributed. The run time resource usage
generated from different clusters is synchronized to maintain a single cloud-wide view of the data
so that a single bill can be created. The authors also proposed a set of accounting system
requirements and an evaluation method which verifies that the solution fulfills these
requirements.
4.4. Security Monitoring and Incident Response
Anand [41] presents a centralized monitoring solution for cloud applications consisting of
monitoring the server, monitors, agents, configuration files and notification components.
Redundancy, automatic healing, and multi-level notifications are other benefits of the proposed
solution which are designed to avoid the typical drawbacks of a centralized monitoring system,
such as limited scalability, low performance and single point of failure.
Brinkmann et al. [42] present a scalable distributed monitoring system for clouds using a
distributed management tree that covers all the protocol-specific parameters for data collection.
Data acquisition is done through specific handler implementations for each infrastructure-level
data supplier. Data suppliers provide interoperability with cloud software, virtualization libraries
and OS-level monitoring tools. The authors review the limitations of existing intrusion detection
systems and discuss VM-level intrusion detection as an emerging area for securing VMs in cloud
environments. The requirements for an efficient intrusion detection system for cloud
infrastructures – including multi-tenancy, scalability and availability – are identified and a VM
introspection detection mechanism via a hypervisor is proposed.
Hypervisor-based cloud intrusion detection systems are a new approach (compared to existing
host-based and network-based intrusion detection systems) that is discussed in [43]. The idea is to
use hypervisor capabilities to improve performance over data residing in a VM. Performance
metrics are defined as networking transmitted and received data, read/write over data blocks, and
CPU utilization. These metrics are retrieved in near real-time intervals by endpoint agents that are
connected directly to a controller that analyzes the collected data using signatures to find any
malicious activity. The controller component sends an alert to a notification service in case there
is any potential attack.
4.5. Security Policy Management
In [44] the authors propose a generic security management framework allowing providers of
cloud data management systems to define and enforce complex security policies through a policy
management module. The user activities are stored and monitored for each storage system, and
are made available to the policy management module. Users’ actions are evaluated by a trust
management module based on their past activities and are grouped as “fair” or “malicious”. An
appropriate architecture for security management which satisfies the requirements of policy
definitions (such as flexibility, expressiveness, extendibility and correctness) has been
implemented. The authors evaluated the proposed system on a data management system that is
built on data storage.
Takabi et al. [45] introduce policy management as a service (PMaaS) to provide users with a
unified control point for managing access policies in order to control access to cloud resources
independently of the physical location of cloud providers. PMaaS is designed specifically to solve

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the issue of having multiple access control authorization mechanisms employed by cloud service
providers that restrict the flexibility of applying custom access control to a particular service. For
this purpose, the PMaaS architecture includes a policy management service provider that is the
entry point for cloud users to define and manage the policies. The cloud service provider imports
the user-defined policies and acts a policy decision point to enforce the user policies.
The challenges associated with policy enforcement in heterogeneous distributed environments are
discussed in [46]. The authors propose a framework to support flexible policy enforcement and a
feedback system using rule- and context-based access control to inform cloud users about the
effect of defined policies. There are three main requirements for building a general policy
enforcement framework. First it must support various data types such as image, structured and
textual data. Secondly, in a distributed environment there need to be several compute engines
such as MR, relational database management systems or clusters. Finally, access policy
requirements in terms of access control policies, data sharing policies, and privacy policies need
to be integrated with the general policy management framework. Several policy enforcement
mechanisms (such as extensible access control markup language or inline-reference monitors to
enforce user-centric policies in accord with cloud provider approval) were also discussed.
In [47] the authors describe A4Cloud with the aim of developing solutions to ensure
accountability and transparency in cloud environments. Users need to be able to track their data
usage to know how the cloud provider satisfies their expectations for data protection. For this
purpose cloud providers must employ solutions that provide users with appropriate control and
transparency over their data, e.g. tools to define policies for compliance with regulatory
frameworks. In another effort [48] the authors discuss the issue of usable transparent data
processing in cloud computing and also consider how to enable users to define transparency
policies over their data. They identify the requirements for transparent policy management in the
cloud based on two aspects: user demands and legal aspects of transparent data processing.
5. BIG DATA SECURITY AND PRIVACY
This section outlines several efforts and projects on big data security and privacy including big
data infrastructures and programming models. It focuses on the Apache Hadoop that is a widely-
used infrastructure for big data projects such as HDFS and Hive, HBase, Flume, Pig, Kafka, and
Storm. We also summarize the state-of-the-art for privacy-preserving data-insensitive solutions in
cloud computing environments.
5.1. Big Data Infrastructures and Programming Models
There are several efforts from the industry to enhance the Hadoop security [66] such as Apache
Rhino [75], Apache Knox [76], Apache Ranger [77] and Apache Sentry [78].
Apache Rhino is an initiative started by Intel at the beginning of 2013 to remarkably enhance the
Hadoop ecosystem security. It aims at providing a framework for Hadoop key management,
authorization, audit and logging [75]. Rhino provides a framework support for encryption and key
management, a common authorization framework for the Hadoop ecosystem, a token based
authentication and single sign on, and it improves audit logging.
Apache Knox (Gateway) is another effort that aims to provide perimeter security for confidential
access to Hadoop clusters through organizational policies within enterprises [76]. Apache Knox
enhances the Hadoop security through simplifying users’ access to the cluster data and job
execution. Client interactions are performed through representational state transfer (REST) Web
services over HTTP. Knox also aims to provide easy integration with existing identity providers

73
and abstracting Kerberos authentication. This is done through encapsulating Kerberos to
eliminate the need for client software or client configuration of Kerberos by clients. In addition, it
provides integration with SAML, open authorization (OAuth) and OpenID.
Apache Ranger proposes a framework for data security across the Hadoop platforms to enable
enterprises run multiple workloads in a multi-tenant environment [77]. Ranger aims to provide
centralized security administration to manage all security related tasks in a central user interface
(UI) or using REST APIs. Fine grained authorization for specific operations through a central UI
is another goal of Ranger. Support for RBAC and attribute-based access control (ABAC), in
addition to centralized auditing services are among the functionalities of this software.
Enforcing fine-grained role based authorization for data and metadata located in a Hadoop cluster
is provided by Apache Sentry [78]. Sentry implements a policy provider to define the access
control. This is done through defining a single global policy file can be used to control access.
There have been also efforts from academia to formulate the security and privacy issues of big
data and also to enhance the security of existing Hadoop distributions. For example, Yu et al. [79]
proposed SEHadoop to enhance the Hadoop security in public clouds by increasing the isolation
level among the Hadoop components and enforcing the least access privilege for various Hadoop
processes. The SEHadoop implements optimized Block Token and Delegation Tokens to avoid
authentication key vulnerability and ensuring fine-grained access control that was discussed in
subsection 3.3.
Recently in [80], Bertino discuss the specific challenges for big data security and privacy. Similar
to traditional information security models, big data solutions must ensure the CIA properties to
ensure secure computing. For example, to ensure integrity, big data platform must enforce
authorization mechanisms to restrict execution of arbitrary MR jobs over multiple datasets from
different owners. Data trustworthiness is another property that ensures accurate analysis for
effective decision making. This work also highlights the privacy challenges including efficiency
of existing cryptographic techniques to ensure privacy of data due to scalability issues within big
data. Trade-off between security and privacy, data ownership and privacy-aware data lifecycle
framework are other privacy challenges that have been discussed.
5.2. Privacy-Preserving Big Data Solutions in the Cloud
Over the time, organizations have collected valuable information about the individuals in our
societies that contain sensitive information, e.g. medical data. Researchers need to access and
analyze such data using big data technologies [63-65] in cloud computing, while organizations
are required to enforce data protection compliance (subsection 3.2).
There has been considerable progress on privacy preservation for sensitive data in both industry
and academia, e.g., solutions that develop protocols and tools for anonymization or encryption of
data for confidentiality purposes. This section categorizes work related to this area according to
different privacy protection requirements. However, these solutions have not yet been widely
adopted by cloud service providers or organizations.
Pearson [69] discusses a range of security and privacy challenges that are raised by
cloud computing. Lack of user control, lack of training and expertise, unauthorized secondary
usage, complexity of regulatory compliance, transborder data flow restrictions and litigation are
among the challenges faced in cloud computing environments. In [28], the authors describe the
privacy challenges of genomic data in the cloud including terms of services of cloud providers
that are not developed with a healthcare mindset, awareness of patient to upload their data into the
cloud without their consent, multi-tenancy, data monitoring, data security and accountability. The

74
authors also provide recommendations for data owners when aiming to use cloud provider
services.
In [49] the authors discussed several privacy issues associated with genomic sequencing. This
study also described several open research problems (such as outsourcing to cloud providers,
genomic data encryption, replication, integrity, and removal of genomic data) along with giving
suggestions to improve privacy through collaboration between different entities and
organizations. In another effort [50], raw genomic data storage through encrypted short reads is
proposed.
Outsourcing privacy is another topic that is discussed in [51]. The authors define the concept of
“outsourcing privacy” where a database owner updates the database over time on untrusted
servers. This definition assumes that database clients and the untrusted servers are not able to
learn anything about the contents of the databases without authorized access. The authors
implement a server-side indexing structure to produce a system that allows a single database
owner to privately and efficiently write data to, and multiple database clients to privately read
data from, an outsourced database.
Homomorphic encryption is another privacy-preserving solution that is based on the idea of
computing over encrypted data without knowing the keys belonging to different parties. To
ensure confidentiality, the data owner may encrypt data with a public key and store data in the
cloud. When the process engine reads the data, there is no need to have the DP’s private key to
decrypt the data. In private computation on encrypted genomic data [52], the authors proposed a
privacy-preserving model for genomic data processing using homomorphic encryption on
genome-wide association studies.
Anonymization is another approach to ensure the privacy of sensitive data. SAIL [53] provides
individual-level information on the availability of data types within a collection. Researchers are
not able to cross-link (which is similar to an equality join in SQL) data from different outside
studies, as the identities of the samples are anonymized. In another effort [57] the authors propose
an integration architecture to make it possible to perform aggregated queries over anonymized
medical data sets from different data providers. In this solution, data providers remove the data
subjects’ identifiers and apply a two-level encryption using hashing and PKI certificates. The
sensitive information will then be anonymized using an open-source toolkit and will be encrypted
granularly using the cloud provider’s public key. ScaBIA [60] is another solution for processing
and storing anonymized brain imaging data in cloud. This approach provides PKI authentication
for administrator roles to deploy a PaaS middleware and defines researchers as users in the in
Microsoft Azure cloud. Researchers are allowed to login by username/password to run statistical
parametric mapping workflows within isolated generic worker containers. The brain imaging
datasets and related results can be shared by the researchers using a RBAC model over secure
HTTPS connections.
In [55], the design and implementation of a security framework for BiobankCloud, a platform that
supports the secure storage and processing of genomic data in cloud computing environments, has
been discussed. The proposed framework is built on the cloud privacy threat modeling approach
[54, 56] which is used to define the privacy threat model for processing next-generation
sequencing data according to the DPD [2]. This solution includes a flexible two-factor
authentication and an RBAC access control mechanism, in addition to auditing mechanisms to
ensure that the requirements of the DPD are fulfilled.

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6. CONCLUSIONS
This paper reviewed several security and privacy issues on big data in the cloud. It described
several big data and cloud computing key concepts such as virtualization, and containers. We also
discussed several security challenges that are raised by existing or forthcoming privacy
legislation, such as the EU DPD and the HIPAA.
The results that are presented in the area of cloud security and privacy are based on cloud
provider activities, such as providing orchestration, resource abstraction, physical resource and
cloud service management layers. Security and privacy factors that affect the activities of cloud
providers in relation to the legal processioning of consumer data were identified and a review of
existing research was conducted to summarize the state-of-the-art in the field.
ACKNOWLEDGEMENTS
This work is funded by the EU FP7 project “Scalable, Secure Storage and Analysis of Biobank
Data” under Grant Agreement no. 317871.
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79
AUTHORS
Ali Gholami is a PhD student at the KTH Royal Institute of Technology. His research
interests include the use of data structures and algorithms to build adaptive data
management systems. Another area of his research focuses on the security concerns
associated with cloud computing. He is currently exploring strong and usable security
factors to enable researchers to process sensitive data in the cloud.
Professor Erwin Laure is Director of the PDC - Center for High Performance
Computing Center at KTH, Stockholm. He is the Coordinator of the EC-funded
"EPiGRAM" and "ExaFLOW" projects as well as of the HPC Centre of Excellence for
Bio-molecular Research "BioExcel" and actively involved in major e-infrastructure
projects (EGI, PRACE, EUDAT) as well as exascale computing projects. His research
interests include programming environments, languages, compilers and runtime
systems for parallel and distributed computing, with a focus on exascale computing.

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