New technologies for data protection

Copyright ©Protegrity Corp.
New Technologies for Data Protection
Arm Innovative
Businesses to
Win
Ulf Mattsson
Chief Security Strategist
www.Protegrity.com

May 2020
Payment Card Industry (PCI)
Security Standards
Council (SSC):
1. Tokenization TaskForce
2. EncryptionTaskForce,Point toPoint
EncryptionTaskForce
3. RiskAssessment
4. eCommerceSIG
5. CloudSIG,VirtualizationSIG
6. Pre-AuthorizationSIG,ScopingSIGWorking
Group
Ulf Mattsson
2
Dec 2019
Cloud Security Alliance
Quantum Computing
Tokenization Management and
Security
Cloud Management and Security
ISACA JOURNAL May 2021
Privacy-Preserving Analytics and
Secure Multi-Party Computation
ISACA JOURNAL May 2020
Practical Data Security and
Privacy for GDPR and CCPA
• Chief Security
Strategist, Protegrity
• Chief Technology
Officer, Protegrity, Atlantic
BT, and Compliance
Engineering
• Head of Innovation,
TokenEx
• IT Architect, IBM
• Develops Industry Standards
• Inventor of more than 70 issued US Patents
• Products and Services:
• Data Encryption, Tokenization, and Data Discovery
• Cloud Application Security Brokers (CASB) and Web Application
Firewalls (WAF)
• Security Operation Center (SOC) and Managed Security Services
(MSSP)
• Robotics and Applications

Data
User App
Key
Management
TEE
AI
Homomorphic
Encryption
Machine
Learning
Quantum
Computing
Differential
Privacy
Innovation
Shared
Security
Model
Data
Security
Policy
Agenda
• Use Cases in Machine learning (ML)
• Secure Multi-Party Computation (SMPC)
• Homomorphic encryption (HE)
• Differential Privacy (DP) and K-Anonymity
• Pseudonymization and Anonymization
• Synthetic Data
• Zero trust architecture (ZTA)
• Zero-knowledge proofs (ZKP)
• Private Set Intersection (PSI)
• Trusted execution environments (TEE)
• Post-Quantum Cryptography
• Regulations and Standards in Data Privacy

Unlockthe Potential of Data Security
- Data Security Governance Stakeholders
4
4
Source: Gartner

Opportunities
Controls
Regulations
Policies
RiskManagement
Breaches
Balance
Protect datainwaysthatare transparent to business processes andcomplianttoregulations
Source: Gartner

https://f.hubspotusercontent10.net/hubfs/8773866/Hitachi%20ID%20Resources/top-it-budget-priorities-through-2020.pdf

Hitachi

Multi-cloud
Policies &
Controls
Security
Privacy
Controls
Policies
Configuration
Regulatory
Compliance
On-premise
Applications, Data and Users
Configuration as code (software)
Cloud provider infrastructure

Increased need for Data Analytics
Data Lake,
ETL, Files
…
U
Analytics, Data Science, AI and ML
Data Pipeline
Data Privacy
Cloud
9
Reduce Risk
• Secure AI & ML
Use-cases
• Analysis
• Insight
• Dashboarding
• Reporting
• Predictions
• Forecasts
• Simulation
• Optimization
Values
• Savings
• Revenue add
Anonymization to minimize the risk of identification
Examples in Banking Credit Card Approval,
• Reducing the risk from 26% down to 8%
• 98% accuracy compared to the Initial Model

Gartner Hype Cycle
for
Emerging
Technologies,
2020
 Time
AI & ML
Gartner

Gartner Hype Cycle for Emerging Technologies, 2020
Algorithmic
Trust
Models Can
Help
Ensure
Data
Privacy
Emerging technologies
tied to algorithmic trust
include
1. Secure access service
edge (SASE)
2. Explainable AI
3. Responsible AI
4. Bring your own
identity
5. Differential privacy
6. Authenticated
provenance
Gartner
11

12

Use Case: Insilico Medicine
An alternative to animal testing for research and development programs in the pharmaceutical industry.
• By using artificial intelligence and deep-learning techniques, Insilico is able to analyze how a compound will affect cells
and what drugs can be used to treat the cells in addition to possible side effects
A comprehensive drug discovery engine, which utilizes millions of samples and multiple data types to discover
signatures of disease and identify the most promising targets for billions of molecules that already exist or can be
generated de novo with the desired set of parameters.
https://insilico.com/
13

Gartner MQ for Data Science and
Machine Learning Platforms
https://www.kdnuggets.com/2020/02/gartner-
mq-2020-data-science-machine-learning.html
Data and analytics pipeline,
including all the following areas:
1. Data ingestion
2. Data preparation
3. Data exploration
4. Feature engineering
5. Model creation and training
6. Model testing
7. Deployment
8. Monitoring
9. Maintenance
10.Collaboration
2020 vs 2019 changes

Digikey, techbrij
Machine Learning Model Lifecycle - Example
1. Define the model: using the Sequential or Model class and add the layers
2. Compile the model: call compile method and specify the loss, optimizer and
metrics
3. Train the model: call fit method and use training data
4. Evaluate the model: call evaluate method and use testing data to evaluate
trained model
5. Get predictions: use predict method on new data for predictions
15

wikipedia
Flux
Keras
MATLAB + Deep
Learning Toolbox
Apache
MXNet
PlaidML
PyTorch
(Facebook)
TensorFlow
(Google)
Open Source
Linux,
MacOS,
Windows
C++
Julia
Python
MATLAB
Perl, Clojure
JavaScript, Go, Scala
Android,
iOS
R
Swift
Machine Learning
Deep-learning
Platforms
Languages
Frameworks & libraries
16

Encryptionand
Tokenization
Discover Data
Assets
Security by
Design
GDPR Security Requirements –Encryption and Tokenization
17

Increased need for data analytics drives requirements.
Data Lake,
ETL, Files
…
• Policy Enforcement Point (PEP)
Protected data fields
U
• Encryption Key Management
U
External Data
Internal
Data
Secure Multi Party Computation
Analytics, Data Science, AI and ML
Data Pipeline
Data Collaboration
Data Pipeline
Data Privacy
On-premises
Cloud
Internal and Individual Third-Party Data Sharing
19

Copyright ©Protegrity Corp. http://homomorphicencryption.org
Use Cases for Secure Multi Party Computation &
Homomorphic Encryption (HE)
20

Use case – Retail - Data for Secondary Purposes
Large aggregator of credit card transaction data.
Open a new revenue stream
• Using its data with its business partners: retailers, banks and advertising companies.
• They could help their partners achieve better ad conversion rate, improved customer satisfaction, and more timely
offerings.
• Needed to respect user privacy and specific regulations. In this specific case, they wanted to work with a retailer.
• Allow the retailer to gain insights while protecting user privacy, and the credit card organization’s IP.
• An analyst at each organization’s office first used the software to link the data without exchanging any of the
underlying data.
Data used to train the machine learning and statistical models.
• A logistic and linear regression model was trained using secure multi-party computation (SMC).
• In the simplest form SMC splits a dataset into secret shares and enables you to train a model without needing to put
together the pieces.
• The information that is communicated between the peers is encrypted at all times and cannot be reverse engineered.
With the augmented dataset, the retailer was able to get a better picture of its customers buying habits.
21

Use case - Financial services industry
Confidential financial datasets which are vital for gaining significant insights.
• The use of this data requires navigating a minefield of private client information as well as sharing data
between independent financial institutions, to create a statistically significant dataset.
• Data privacy regulations such as CCPA, GDPR and other emerging regulations around the world
• Data residency controls as well as enable data sharing in a secure and private fashion.
Reduce and remove the legal, risk and compliance processes
• Collaboration across divisions, other organizations and across jurisdictions where data cannot be
relocated or shared
• Generating privacy respectful datasets with higher analytical value for Data Science and Analytics
applications.
22

Use case: Bank - Internal Data Usage by Other Units
A large bank wanted to broaden access to its data lake without compromising data privacy, preserving the data’s
analytical value, and at reasonable infrastructure costs.
• Current approaches to de-identify data did not fulfill the compliance requirements and business needs, which had
led to several bank projects being stopped.
• The issue with these techniques, like masking, tokenization, and aggregation, was that they did not sufficiently
protect the data without overly degrading data quality.
This approach allows creating privacy protected datasets that retain their analytical value for Data Science and business
applications.
A plug-in to the organization’s analytical pipeline to enforce the compliance policies before the data was consumed by
data science and business teams from the data lake.
• The analytical quality of the data was preserved for machine learning purposes by-using AI and leveraging privacy
models like differential privacy and k-anonymity.
Improved data access for teams increased the business’ bottom line without adding excessive infrastructure costs,
while reducing the risk of-consumer information exposure.
23

https://royalsociety.org
Secure Multi-Party Computation (MPC)
Private multi-party machine learning with MPC
Using MPC, different
parties send
encrypted messages
to each other, and
obtain the model
F(A,B,C) they wanted
to compute without
revealing their own
private input, and
without the need for a
trusted central
authority.
Secure Multi-Party machine learning
Central trusted authority
A B C
F(A, B,C)
F(A, B,C) F(A, B,C)
U
B
A C
F(A, B,C)
U U
U
24

Case Study – HE and Securely sharing sensitive information
An example from the healthcare domain.
The recent ability to fully map the human genome has opened endless possibilities for advances in
healthcare.
1. Data from DNA analysis can test for genetic abnormalities, empower disease-risk analysis, discover family
history, and the presence of an Alzheimer’s allele.
• But these studies require very large DNA sample sizes to detect accurate patterns.
2. However, sharing personal DNA data is a particularly problematic domain.
• Many citizens hesitate to share such personal information with third-party providers, uncertain of if,
how and to whom the information might be shared downstream.
3. Moreover, legal limitations designed to protect privacy restrict providers from sharing this data as well.
4. HE techniques enable citizens to share their genome data and retain key privacy concerns without the
traditional all-or-nothing trust threshold with third-party providers.
26

https://royalsociety.org
Homomorphic encryption (HE)
HE depicted in a client-server model
• The client sends encrypted
data to a server, where a
specific analysis is performed
on the encrypted data,
without decrypting that data.
• The encrypted result is then
sent to the client, who can
decrypt it to obtain the
result of the analysis they
wished to outsource.
Encryption of x
Client
Server
Analysis
Encrypted F(x)
• Policy Enforcement Point (PEP)
U
• Encryption Key Management
27

Trusted execution environments
Trusted Execution Environments (TEEs) provide secure computation capability through a combination of special-purpose
hardware in modern processors and software built to use those hardware features.
The special-purpose hardware provides a mechanism by which a process can run on a processor without its memory or
execution state being visible to any other process on the processor,
• not even the operating system or other privileged code.
*: Source: http://publications.officialstatistics.org
Computation in a TEE is not
performed on data while it
remains encrypted.
• Typically, the memory space
of each TEE (enclave)
application is protected from
access
• AES-encrypted when
and if it is stored off-
chip.
Usability is low and products/services are emerging in MS Azure, IBM’s cloud service Amazon AWS (late 2020)*
29

Random
differential
privacy
Probabilistic
differential
privacy
Concentrated
differential
privacy
Noise is very low.
Used in practice.
Tailored to large numbers
of computations.
Approximate
differential
privacy
More useful analysis can be performed.
Well-studied.
Can lead to unlikely outputs.
Widely used
Computational
differential privacy
Multiparty
differential
privacy
Can ensure the privacy of individual contributions.
Aggregation is performed locally.
Strong degree of protection.
High accuracy
6 Differential
Privacy
Models
A pure model provides protection even against attackers with
unlimited computational power.
In differential
privacy, the
concern is about
privacy as the
relative difference
in the result
whether a
specific individual
or entity is
included in the
input or excluded
31

k-Anonymity
This second table shows the data anonymised to achieve
k-anonymity of k = 3, as you can see this was achieved by
generalising some quasi-identifier attributes and
redacting some others.
For k-anonymity to be achieved, there need to be at least
k individuals in the dataset who share the set of
attributes that might become identifying for each
individual.
K-anonymity might be described as a ‘hiding in the
crowd’ guarantee: if each individual is part of a larger
group, then any of the records in this group could
correspond to a single person.

Zero Trust Architecture (ZTA)
• Understanding who the users are, which applications they are using and how they are connecting
• Enforce policy that ensures secure access to your data
• Controls close to the protect surface as possible, creating a microperimeter around it
• This micro-perimeter moves with the protect surface, wherever it goes
• Continue to monitor and maintain in real time
NIST
Zero-knowledge
proofs (ZKP) are
privacy-preserving
messaging protocols
• Different from
ZTA concept

Private Set
Intersection
(PSI)
Identifies the
common customers
without disclosing
any other
information
This replaces simplistic approaches such as one-way hashing functions that are susceptible to dictionary attacks.
Applications for PSI include identifying the overlap with potential data partners (i.e. “Is there a large enough client
base in common to be worthwhile to work together?”), as well as aligning datasets with data partners in
preparation for using MPC to train a machine learning model

Data protection techniques: Deployment on-premises, and clouds
Data
Warehouse
Centralized Distributed
On-
premises
Public
Cloud
Private
Cloud
Vault-based tokenization y y
Vault-less tokenization y y y y y y
Format preserving
encryption
y y y y y
Homomorphic encryption y y
Masking y y y y y y
Hashing y y y y y y
Server model y y y y y y
Local model y y y y y y
L-diversity y y y y y y
T-closeness y y y y y y
Privacy enhancing data de-identification
terminology and classification of techniques
De-
identification
techniques
Tokenization
Cryptographic
tools
Suppression
techniques
Formal
privacy
measurement
models
Differential
Privacy
K-anonymity
model
36

Differential
Privacy
(DP)
2-way
Format
Preserving
Encryption
(FPE)
Homomorphic
Encryption
(HE) K-anonymity
model
Tokenization
Static
Data
Masking
Hashing
1-way
Data store
Clear Text
Data Source
Algorithmic
Random Noise added
Format
Preserving
Fast Slow
Very
slow Fast Fast
Format
Preserving
Dynamic Data
Masking
Anonymization
Of Attributes
Pseudonymization
Of Identifiers
Fastest
User
Fast
Synthetic
Data
Static
Derivation
Computing
on encrypted
data

2-way
Format
Preserving
Encryption
(FPE)
Homomorphic
Encryption
(HE)
Tokenization
Data store
Clear Text
Data Source
Algorithmic
Random
Format
Preserving
Fast Slow
Very
slow
Dynamic Data
Masking
Pseudonymization
Of Identifiers
Fastest
User
Computing
on encrypted
data
Quantum Computers?
• Quantum computers and other strong
computers can break algorithms and
patterns in encrypted data.
• We can instead use random numbers to
secure sensitive data.
• Random numbers are not based on an
algorithm or pattern that computers can
break.
Tech giants are building their own machines and
speeding to make them available to the world as a
cloud computing service. In the competition: IBM,
Google, Microsoft, Intel, Amazon, IonQ, Quantum
Circuits, Rigetti Computing

Differential
Privacy
(DP)
K-anonymity
model
Static
Data
Masking
Hashing
1-way
Data store
Clear Text Data Source
Noise
added
Fast Fast
Format
Preserving
Dynamic Data
Masking
Anonymization
Of Attributes
User
Fast
Static
Derivation
Example of Data Generalization
Non-reversable Data
Transformations
Synthetic
Data

Secure AI– Use Case withSyntheticData
40

Differential Privacy (DP)
2-way
Format Preserving Encryption (FPE) K-anonymity
Tokenization
1-way
Data Store
Clear Text
Data Source
Algorithmic
Random Noise added
Format
Preserving
Fast Slow Fast
Format
Preserving
Anonymization
Of Attributes
Pseudonymization
Of Identifiers
Fastest
User
Fast
Synthetic
Data
Static
Derivation
Example: Data Privacy for Machine Learning

Secure AI & ML
Use-cases
• Analysis
• Insight
• Dashboarding
• Reporting
• Predictions
• Forecasts
• Simulation
• Optimization
Values
• Savings
• Revenue add
Anonymization to minimize the risk of identification
• Examples in Banking Credit Card Approval,
• Reducing the risk from 26% down to 8%
• 98% accuracy compared to the Initial Model
Pseudonymization and Anonymization

Post-quantum cryptography
Time
frame
Area Comment
Short
Upgrade to AES, preferably AES-256 with
strong random seed
Immediate-medium step
Short Use SHA-512 for hashing Immediate step
Short
Use stateful hash-based signatures for
signing
Immediate review
Short
Use hybrid cryptography to protect against
both weaknesses in RSA/ECC and potential
weaknesses in post-quantum algorithms
Immediate steps
Medium Lattice based algorithms Tools study and integration plan
Medium Homomorphic Encryption Tools and partner integration
Medium Operation on encrypted data Integration of protocols
Medium Secure Multi-party Computing (SMPC) Integration of protocols
Medium
2022 NIST to complete review of quantum
safe algorithms
Tools integration
Medium 2022 NIST Standards to be released. Tools integration
Long
2024 Industry standards based on NIST
algorithms from NIST Standards
Tools integration
Long Analytics and Machine Learning ML algorithms optimized for Quantum processors
Long Full industry adoption 2019+ Tools integration
The future of Cryptography

Post-quantum cryptography research is
mostly focused on these approaches
1. Lattice-based cryptography This approach includes
cryptographic systems such as learning with errors,
including NTRU.
2. Supersingular elliptic curve Isogeny cryptography This
cryptographic system relies on the properties of
supersingular elliptic curves and supersingular isogeny
graphs to create a Diffie-Hellman replacement with forward
secrecy.
3. Code-based cryptography This includes cryptographic
systems which rely on error-correcting codes, such as the
McEliece and Niederreiter encryption algorithms.
4. Multivariate cryptography This includes cryptographic
systems such as the Rainbow (Unbalanced Oil and Vinegar)
scheme which is based on the difficulty of solving systems
of multivariate equations.
5. Hash-based cryptography alternative to number-theoretic
digital signatures like RSA and DSA.
6. Symmetric key quantum resistance Provided one uses
sufficiently large key sizes.

Risk
Reduction
Source:
INTERNATIONAL
STANDARD ISO/IEC
20889
46

Personally Identifiable Information(PII) in compliance with the
EUCross Border Data Protection Laws, specifically
• Datenschutzgesetz 2000(DSG 2000)in Austria, and
• Bundesdatenschutzgesetz inGermany.
This requiredaccess to Austrianand German customer data to
berestricted to onlyrequesters ineach respective country.
• Achieved targeted compliance with EU Cross Border Data
Security laws
• Implemented country-specificdata access restrictions
Datasources
Case Study
Amajor international bankperformed a consolidationofallEuropeanoperationaldatasources
to Italy
47

Lower Risk andHigher Productivity with More AccesstoMoreData
48

PaymentApplication
Payment
Network
Payment
Data
Policy, tokenization,
encryption
and keys
Gateway
Call Center
Application
PI*Data
Salesforce
Analytics
Application
DifferentialPrivacy
AndK-anonymity
PI*Data
Microsoft
ElectionGuard
Election
Data
Homomorphic Encryption
DataWarehouse
PI*Data
Vault-less tokenization
Use-Cases of Some Data Privacy Techniques
Voting
Application
Dev/testSystems
Masking
PI*Data
Vault-less tokenization
49

A DataSecurityGateway Can Protect Sensitive Datain Cloud and On-premise
50

Cloud Security Logical Architecture
Gartner

Cloud Security Architecture
Gartner

Big DataProtectionwith GranularField Level Protectionfor GoogleCloud
53

Use Case (Financial Services) - Compliance with Cross-Border and Other
Privacy Restrictions
54

Use this shape toput
copy inside
(you can change the sizing tofit your copy needs)
Protection ofdata
in AWS S3 with Separation ofDuties
• Applications can use de-identified
data or data inthe clear based on
policies
• Protection of data inAWSS3 before
landing in a S3 bucket
Separation of Duties
• EncryptionKeyManagement
• PolicyEnforcementPoint(PEP)
55

Securosis, 2019
Consistency
• Most firmsarequite familiar with their on-premises
encryption andkeymanagement systems, so they often
prefer toleverage the same tool and skills across multiple
clouds.
• Firms often adopt a “best of breed”cloud approach.
Data SecurityManagement forHybrid Cloud
Trust
• Some customers simply donot trusttheir vendors.
Vendor Lock-in and Migration
• A commonconcern is vendorlock-in, andan
inabilitytomigratetoanothercloud serviceprovider.
Google Cloud AWSCloud Azure Cloud
Cloud Gateway
S3 Salesforce
Data Analytics
BigQuery
56

IS: International
Standard
TR: Technical Report
TS: Technical
Specification
Guidelines to help
comply with ethical
standards
20889 IS Privacy enhancing de-identification terminology and
classification of techniques
27018 IS Code of practice for protection of PII in public clouds acting
as PII processors
27701 IS Security techniques - Extension to ISO/IEC 27001 and
ISO/IEC 27002 for privacy information management - Requirements
and guidelines
29100 IS Privacy framework
29101 IS Privacy architecture framework
29134 IS Guidelines for Privacy impact assessment
29151 IS Code of Practice for PII Protection
29190 IS Privacy capability assessment model
29191 IS Requirements for partially anonymous, partially unlinkable
authentication
Cloud
11 Published International Privacy Standards
Framework
Management
Techniques
Impact
19608 TS Guidance for developing security and privacy functional
requirements based on 15408
Requirements
27550 TR Privacy engineering for system lifecycle processes
Process
ISO Privacy Standards
57

References A:
1. C. Gentry. “A Fully Homomorphic Encryption Scheme.” Stanford University. September 2009,
https://crypto.stanford.edu/craig/craig-thesis.pdf
2. Status Report on the Second Round of the NIST Post-Quantum Cryptography Standardization Process,
https://csrc.nist.gov/publications/detail/nistir/8309/final
3. ISO/IEC 29101:2013 (Information technology – Security techniques – Privacy architecture framework)
4. ISO/IEC 19592-1:2016 (Information technology – Security techniques – Secret sharing – Part 1: General)
5. ISO/IEC 19592-2:2017 (Information technology – Security techniques – Secret sharing – Part 2: Fundamental
mechanisms
6. Homomorphic Encryption Standardization, Academic Consortium to Advance Secure Computation,
https://homomorphicencryption.org/standards-meetings/
7. Homomorphic Encryption Standardization, https://homomorphicencryption.org/
8. NIST Post-Quantum Cryptography PQC, https://csrc.nist.gov/Projects/Post-Quantum-Cryptography
9. UN Handbook on Privacy-Preserving Computation Techniques,
http://publications.officialstatistics.org/handbooks/privacy-preserving-techniques-
handbook/UN%20Handbook%20for%20Privacy-Preserving%20Techniques.pdf
10. ISO/IEC 29101:2013 Information technology – Security techniques – Privacy architecture framework,
https://www.iso.org/standard/45124.html
11. Homomorphic encryption, https://brilliant.org/wiki/homomorphic-encryption/ 58

UlfMattsson
Chief SecurityStrategist
www.Protegrity.com
Thank You!

Area Timing Focus Comments
Requirements Short Internal requirements International regulations
Cloud Short Machine Learning Start with basic ML training and inference on senstivie data in cloud
Competition Short Competitive advantage ML and NLP-powered services can give banks a competitive edge
Short Encrypted data Important
Long Synthetic data Computing cost?
Medium AML / KYC What are other Large banks doing?
Short Analytics Initial focus
Short
Operation on encrypted
data
Computation on sensitive data to the cloud. Trade-offs with performance, protection and utility?
Industry Short Industry dialog Working groups in standard bodies (ANSI X9, Cloud Security Alliance, Homomorphic Encryption Org)
Model Short Encrypted model Important
Short Experimentation What are other Large banks doing?
Short Scotia Bank case study Query solution for AML / KYC
Proven Medium Fast follower What are some proven solutions?
Short
Homomorphic
Encryption post-
Lattice-based cryptography is a promising post-quantum cryptography family, both in terms of
foundational properties as well as its application to both traditional and homomorphic encryption
Medium Quantum Plan for quantum safe algorithms
Long Quantum Plan for quantum ML algorithms
Sharing Short
Secure Multi-party
Computing (SMPC)
Without revealing their own private inputs and outputs. Encrypted data and encryption keys never
comingled while computation on the encrypted data is occurring or an encryption key is split into
shares
Short Vendor positioning
Nonlinear ML regression needed? Linear Regression is one of the fundamental supervised-ML. Linear
and non-linear credit scoring by combining logistic regression and support vector machines
Short Framework integration Important
3rd party Long 3rd party integration Mining first
Long Federated learning Complicated
Long TEE Emerging
Analytics
Data
Quantum
Solutions
Training ML
Pilot
Use case: Bank

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