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New technologies for data protection
1.
Copyright ©Protegrity Corp. New
Technologies for Data Protection Arm Innovative Businesses to Win Ulf Mattsson Chief Security Strategist www.Protegrity.com
2.
Copyright ©Protegrity Corp. 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
3.
Copyright ©Protegrity Corp. 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
4.
Copyright ©Protegrity Corp. Unlockthe
Potential of Data Security - Data Security Governance Stakeholders 4 4 Source: Gartner
5.
Copyright ©Protegrity Corp. Opportunities Controls Regulations Policies RiskManagement Breaches Balance Protect
datainwaysthatare transparent to business processes andcomplianttoregulations Source: Gartner
6.
Copyright ©Protegrity Corp. https://f.hubspotusercontent10.net/hubfs/8773866/Hitachi%20ID%20Resources/top-it-budget-priorities-through-2020.pdf
7.
Copyright ©Protegrity Corp. Hitachi
8.
Copyright ©Protegrity Corp. Multi-cloud Policies
& Controls Security Privacy Controls Policies Configuration Regulatory Compliance On-premise Applications, Data and Users Configuration as code (software) Cloud provider infrastructure
9.
Copyright ©Protegrity Corp. 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
10.
Copyright ©Protegrity Corp. Gartner
Hype Cycle for Emerging Technologies, 2020 Time AI & ML Gartner
11.
Copyright ©Protegrity Corp. 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.
Copyright ©Protegrity Corp. 12
13.
Copyright ©Protegrity Corp. 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
14.
Copyright ©Protegrity Corp. 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
15.
Copyright ©Protegrity Corp. 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
16.
Copyright ©Protegrity Corp. 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
17.
Copyright ©Protegrity Corp. Encryptionand Tokenization Discover
Data Assets Security by Design GDPR Security Requirements –Encryption and Tokenization 17
18.
Copyright ©Protegrity Corp. 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
19.
Copyright ©Protegrity Corp. 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
20.
Copyright ©Protegrity Corp.
http://homomorphicencryption.org Use Cases for Secure Multi Party Computation & Homomorphic Encryption (HE) 20
21.
Copyright ©Protegrity Corp. 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
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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
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Copyright ©Protegrity Corp. 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
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Copyright ©Protegrity Corp. 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) Protected data fields U B A C F(A, B,C) U U U 24
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Copyright ©Protegrity Corp. 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
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Copyright ©Protegrity Corp. 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
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Copyright ©Protegrity Corp. 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) Protected data fields U • Encryption Key Management 27
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Copyright ©Protegrity Corp. 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
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Copyright ©Protegrity Corp. 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
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Copyright ©Protegrity Corp. 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
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Copyright ©Protegrity Corp. 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
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Copyright ©Protegrity Corp. 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.
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Copyright ©Protegrity Corp. 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
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Copyright ©Protegrity Corp. 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
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Copyright ©Protegrity Corp. 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
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Copyright ©Protegrity Corp. 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
37.
Copyright ©Protegrity Corp. 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
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Copyright ©Protegrity Corp. 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
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Copyright ©Protegrity Corp. 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
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Copyright ©Protegrity Corp. Secure
AI– Use Case withSyntheticData 40
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Copyright ©Protegrity Corp. 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
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Copyright ©Protegrity Corp. 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
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Copyright ©Protegrity Corp. 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
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Copyright ©Protegrity Corp. 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
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Copyright ©Protegrity Corp. 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.
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Copyright ©Protegrity Corp. Risk Reduction Source: INTERNATIONAL STANDARD
ISO/IEC 20889 46
47.
Copyright ©Protegrity Corp. 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
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Copyright ©Protegrity Corp. Lower
Risk andHigher Productivity with More AccesstoMoreData 48
49.
Copyright ©Protegrity Corp. 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
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Copyright ©Protegrity Corp. A
DataSecurityGateway Can Protect Sensitive Datain Cloud and On-premise 50
51.
Copyright ©Protegrity Corp. Cloud
Security Logical Architecture Gartner
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Copyright ©Protegrity Corp. Cloud
Security Architecture Gartner
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Copyright ©Protegrity Corp. Big
DataProtectionwith GranularField Level Protectionfor GoogleCloud 53
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Copyright ©Protegrity Corp. Use
Case (Financial Services) - Compliance with Cross-Border and Other Privacy Restrictions 54
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Copyright ©Protegrity Corp. 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
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Copyright ©Protegrity Corp. 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
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Copyright ©Protegrity Corp. 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
58.
Copyright ©Protegrity Corp. 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
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SecurityStrategist www.Protegrity.com Thank You!
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Copyright ©Protegrity Corp. 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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