[Technology Toolkit]
In the Age of When Customer Data Must Be Treated as a King, PET Will Take on the Responsibility

Technology Toolkit 2021 is a technical white paper describing core technologies that are being researched and developed by Samsung SDS R&D Center. We would like to introduce in this paper a total of seven technologies concerning AI, Blockchain, Cloud, and Security with details on their technical definition, key features, differentiating points, and use cases to give our readers some insights into our work.

Privacy Enhancing Technology

 Download the report

1. Introduction to Technology

Technology Trends and Background

In a world that revolves around data, protecting one’s privacy, and by extension the data itself, is becoming not an option but a necessity. In response to this trend, global laws/regulations such as Personal Information Protection Act (Korea), GDPR (EU), and CCPA (USA, California) have been modified to beef up privacy protection, and a new market is emerging that supports data utilization in privacy-protected environment.

Privacy protection technology, commonly referred to as de-identification or anonymization technology, has been implemented using relatively intuitive methods such as identifier removal and generalization. However, the latest law/regulation reinforcement and increased requirements concerning data utilization have led to more theoretical and quantifiable technologies fitted with cryptographic function and a number of international standards have been proposed pertaining to the matter.^[1]

In this paper, we will give you an overview of encryption-based privacy enhancing technology, a technology that is being actively adopted by global companies such as Google, Microsoft, IBM, Intel, and Ant Financial and numerous startups for sectors like finance, healthcare, and marketing. We hope to use our technology to help companies as well as individuals to protect their private information.

Data = Money

We can Create high-added values using data. However, there is a threat of personal information exposure.

Privacy-related Laws/Regulation

CAlifornia Consumer Privac act Logo, PIPEDA Logo

GDPR(General Data Protection Regulation)

Privacy Protection-oriented Business

Use Privacy protection technology to seek new business to deal with new security threats and Policy

Logos: Micorsoft, Google, SAP, Intel, inpher, Alibaba Group, Duolify

[Figure 1] Privacy Issue

Definition

PET is an encryption-based privacy enhancing technology that defeated the limitations of existing technologies such as re-identification risk and analytical value degradation. Traditionally, cryptographic technologies have been applied to areas such as data encryption, digital signatures, and safe cryptographic protocols to protect corporate security rather than individual privacy. However, following changes led by the age of 4th industrial revolution, 5G, and data-driven technology, the encryption-based anonymization technology - theoretically researched for more than 10 years – has come under spotlight for its business potentiality.

Representative technologies include 1) homomorphic encryption technology that enables computation in encrypted state, 2) differential privacy technology that can quantify the level of privacy, and 3) synthetic data generation technology that can create fake data with statistical and probabilistic characteristics that are similar to the original one. Needless to say, it takes time to assimilate these technologies but for the sake of this paper, here we will take a brief look into these three different technologies in terms of their definition and main features.

Limitations of existing de-identification technology

'We need technologies that can preserve data value and privacy at the same time'

Key De-identification Technologies

1. Pesudonymization - Hashing

2. Generalization - Rounding

3. Randomization - Noise Addition

4. Advanced tools' - Homomorphic Encryption - Synthetic Data/Differential Privacy

[Figure 2] Privacy Enhancing Technology

① HE: Homomorphic Encryption

Homomorphic encryption is an encryption technology that supports data analysis in an encrypted state. For example, as shown in [Figure 3], each ciphertext that homomorphically encrypted 2 and 5 is seen as a random number and if you either add or multiply these numbers and decrypt the result, you will get 7(=2+5) or 10(=2x5). Now if you apply a general encryption technology such as AES, you would get a completely different value when the computation result of ciphertext is decrypted, whereas with homomorphic encryption, you would get a computation result that’s exactly the same as that of original data.

Preservation of Addition Computation

• 2 + 5 = 7
• e + e = D
• E(2) + E(5) = E(2)+E(5)

Preservation of Multiplication Computation

• 2 * 5 = 7
• e * e = D
• E(2) * E(5) = E(2)*E(5)

'Homomorphic; encryption is next-generation encryption technology that reserves addition/multiplication computation to enable data analysis using encrypted data'

[Figure 3] Homomorphic Encryption

Some of homomorphic encryption technology attributes can be found among the cryptographic technologies that are currently in use. Even in the 70s, there were partially homomorphic encryption technologies available that supported either multiplication or addition computation - such as RSA ciphers widely used in areas such as public certificates and HTTPS, or the international standard Paillier ciphers.

Fully homomorphic encryption technology preserving both addition and multiplication computation was first proposed by IBM researcher Gentry in 2009 and since then, active research has been carried out to date. Fully homomorphic encryption preserves both addition and multiplication computation, allowing you to preserve computations of all functions that are to be applied to data analysis, machine learning and AI analysis.^[2]

② DP: Differential Privacy

Let's say you are making a query in a database. In this particular situation, if there is a significant difference in the responses between when the database contains person A’s private information and when it is not, there is a risk of exposing his/her privacy. For example, if you are making a query on average annual salary in a database containing annual salary information, and no measures are taken to protect one’s privacy, you will be able to work out person A’s exact annual salary by accounting for value difference between when his/her personal information is included in the database and when it is not. This is where differential privacy protection technology can demonstrate its merit with its ability to mix in the right distribution of noises into response to prevent you from computing person A’s annual salary.

Query(Function K)

Average? Maximum Valju? Machine learning result?

Differental privacy

• DB 1
• A in 1
• Includes info A in DB

Unable to distinguish analytics result Differental privacy

• DB 2
• A out 1
• Does not include info a in DB

Technology that eliminates the risk of personal information leakage

caused by the difference in the analytics results of two subtly different database

[Figure 4] Differential Privacy Protection

This technology, first took its concrete form by Microsoft researcher Dwork in 2006. It offers a great technical advantage in that it can measure in numeric numbers how much data processing results are damaged (error radius) and how much privacy can be protected. Following recent announcement by Google and Apple of using differential privacy technology for their respective Chrome Browser and iPhone/iCloud/Uber in the course of promoting their privacy protection level, this differential privacy technology has been attracting a lot of attention.

③ SD: Synthetic Data

Synthetic data adopts a de-identification method that creates fake data (virtual data) so that data can be safely analyzed and utilized without compromising the security of personal information contained in original data. This is similar to Deep Fake where realistic fake videos are created using artificial intelligence to compose and manipulate videos/photos.

Research on synthetic data first took off in 1981 when professor Rubin from Statistics Department at Harvard University adopted synthetic data as a means to substitute missing values like in surveys. The research has since been expanded to include fully synthetic data (all public data is fake data), partially synthetic data (only some information in public data is fake data) and complex synthetic data (data newly created using partially synthetic data).

Methods for creating synthetic data include traditional statistical method, machine learning model (GAN: Generative Adversarial Networks) application method, and differential privacy protection application method for fake data privacy assurance.

Machine learning Model(GAN) Application Method

• Actual Data
• Q: What is the real data?
• Discriminatior
• Answer
• Feedback
• imitation Data
• Generator

Privacy Protection Technology

• NIST Logo
• Differential Privacy Synthetic Data Challenge

Virtual data generation technologh with statistical properties similar to the original data

Security can be enhanced with differential privacy techonlogy and holomorphic encryption technology

[Figure 5] Synthetic Data

2. Key Features

PET provides multiple connecting functions to ensure privacy protection using the world's best homomorphic encryption, differential privacy protection and synthetic data technology.

No data loss and no data leakage

Existing de-identification technologies modify original text data to avoid exposure of personal information, however they lose meaningful data in the process. In addition, because data are morphed according to the objectives of analysis, it makes it difficult to use the data for other analytical purposes. However, because homomorphic encryption technology encrypts and utilizes the original data without losing them, it allows more accurate analysis and prevents original data from being leaked at its source.

Basic statistical/machine learning/deep learning functions

Homomorphic encryption technology supports basic statistical function as well as various machine learning training & inference function and deep learning inference function. In particular, it can provide an optimal solution suitable to the needs of our customers by incorporating various PET technologies depending on application environment.

Safe data transmission to other organizations and their utilization

Synthetic data is an imitation data created using the original data from data holder, and it can be used freely without restrictions of privacy protection laws and regulations. In particular, for researchers and developers who have had difficulty collecting data due to security measures imposed by data holder, synthetic data can provide them with means to explore and learn data and develop analysis functions needed. The technology will provide developers with an opportunity to implement analytics solutions in real situations for sectors like finance, healthcare and security industry that handle sensitive data and acquire know-hows over the course of time.

No Data Loss/No Leakage

Performs analysis accurately as information in the original text is encrypted and utilized without lass

Prevents data leakage at its original source

Homomorphic Encrypted Data Analysis

Homomorphic Encrypted Data, Analytics Service, Homomorphic Encrypted Data

Provides Multiple Analytics Function

Supports basic statistics, machine learning/deep learning function

Providers best solutions tllored to customor's need by combining multiple PET techonologies according to the application environment

AI Analytics Platform

Brightics Ai

Jupyter Notebook

AI Analytics Function

Basic Solution

machine learning

deep learning

Homomorphic Encryption Primitive

Safe Data Transmission/ Usage

Allows on to freely use fake data created using the original data without having to worry about regulatory issues

Various analysis are viable in industrial field dealing with sensitive data

• Input Data
• Synthetic Data Generatior
• Output Data
• Data Ultilizaiton

[Figure 6] Key Features of PET

3. Differentiating Points

Our homomorphic encryption technology is the world-best. We secured and refined original PET technology recognized at top-tier conferences like Eurocrypt'18 and Asicrypt'19, and we used this enhanced technology to win iDash, an international Encrypted Analysis Support Competition in 2020.^[3] And since then, our technology has been refined further as proven at AAA’19 where our analysis result yielded 10 times faster analysis speed and same accuracy compared to the cutting-edge machine learning training. Our PET technology offers the world's best efficiency with its optimal approximate computation and concurrent processing of tens of thousands of data and it yields over 99.99% consistency in analysis result compared to the original data.

Collaboration w/ Seoul National University Crypto Lab

Verified by academic world - presented at top-tier academic conferencess including Eurocrypt '18, Asiacrypt '17

Encrypted analytics techonlogy verified by int'l competitions - Won'17 iDash - After 2018, all iDASH qualifiers used Seoul Uni's technology

Proprietary Technologiey enhancment and External Verification Status(2020)

Provides same accuradcy and 10 times + analytics speed compared to cutting-edge ML trainig (AAA'19)

Our encrypted Analytics Technology was verified and recognized by and international competition targeting Proprietary technologies - Won '20 iDASH

Internation Genome information Analysis Competition Won 1st place in 'iDASH 2020' IDASH Privacy and Security Workshop 2020 - secure genome analysis competition

[Figure 7] Performance and Differentiating Points

4. Use Cases

Case 1 : Homomorphic encryption-based predictive service

When an institution (data holder) wants to use high-performance analysis model owned by another institution (service provider), the service provider in possession of the model may not be very forthcoming with sharing their model, given that it is their important asset. In addition, the data holder is prevented from transferring their data outside their premises due to privacy reason. This is where our homomorphic encryption technology can be useful as it can provide a predictive service that can keep the analysis model from leaking whilst protecting customer data.

Pain Point(ML Inference)

• Service Provider
• Concerns about analytics model leakage

• User
• Concerns about personal information leakage

Credit Rating Model

• Analytics Model
• Encrypted Analytics Service

Homomorphic encrypted query Homomorphic encrypted answer Credit-related Personal Data

• Personal Info

[Figure 8] Homomorphic Encryption-based Predictive Service Use Case

Case 2 : creating synthetic data capable of controlling privacy protection level

When using data containing sensitive personal information such as financial transaction and medical records, pseudonymization is required to prevent leakage. Here, deep learning-based synthetic data technology can be used to obtain meaningful analysis result from pseudonym information created by training on specific features of the original data. In addition, differential privacy protection technology can be used to control the level of protection when generating synthetic data.

1. Original Data Transfer - Raw Data: Data Source 2. Synthetic Data Generation - Prereparation, DP-GAN: Samsung SDS (Privacy Budget Selection, Differential private GAN(DP-GAN), Synthetic Data) 3. Synthetic Data Transfer - Privacy Preserving Synthetic Data: customer, Analysis on Synthetic Data

[Figure 9] Pseudonymization Service using Synthetic Data Generation Technology Use Case

5. Business Cases

Now let’s take a look at some of the cases where we adopted PET in actual business.^[4]

Credit rating analysis for financial sector

Technology verification was conducted to test our PET technology for its ability to predict credit rating using actual customer data held by domestic financial companies. The data was provided to service provider in their encrypted state, so the personal information of customers were not exposed at all in credit score prediction and analysis process. In addition, the credit rating results were derived in their encrypted state and could only be verified by users with decryption keys.

The results of our technology verification were as follows. We were able to confirm that the accuracy of our homomorphic encryption-based analysis was exactly the same as that of original data-based analysis and that our technology provided excellent analysis speed, processing millions of data in 12 hour-time frame, thereby proving once again its applicability to real environment.

Chronic disease recurrence prediction for medical sector

The second case covers medical sector. In collaboration with domestic hospital, we developed a homomorphic encryption technology suitable for deep learning-based model that is adept at predicting recurring chronic disease.

Our technology verification showed that encrypted analysis delivered almost the same accuracy to non-encrypted analysis, and it took an average of 30 seconds to perform an analysis of each medical case. It also showed that if we apply homomorphic encryption parallelization to the process, we could reduce processing time to less than 1 second per case, which is good enough for on-site application.

[Finance] Credit Rating Analysis

Target: predict credit rating based on customer transaction data

Objective: compare prediction accuracy between plain=text (ofiginal data) and cryptographic data

Application Model: machine learing (Decision Tree)

Result: Achieved same accuracy/ 1 million cases processed in 12hrs

[Health Care] Severe Disease Recurrence Prediction

Target: predict cancer recurrence period based on visit records of cancer patients

Objective: compare prediction accuracy between plain-text (original data) and cryptographic data

Appliction Model: deep-learning (RNN-GRU)

Result: achieved same accuracy/ a single case takes 30 secs (takes less than 1 sec with parallel processing)

[Figure 10] Finance and Medical Business Case

6. Closing

These use cases helped us confirm how important it is to protect our customers’ sensitive data and their assets (high-performance analysis models) and how worried they are about possible institutional and legal risks that may come with new services offered using their sensitive data/assets. Moreover, we could see how important it is to these concerning customers that we provide PET-based analysis solution that can accommodate high-quality services and also avoid data/asset leakage at their sources.

If we could provide PET-based analysis service that is both user-friendly and is adept at responding quickly to the needs of various industries, countless number of data that are sitting idly away at the moment could be used towards new businesses more aggressively.

# References
[1] ISO/IEC 20889:2018, Privacy enhancing data de-identification terminology and classification of techniques
[2] https://www.samsungsds.com/kr/insights/GDPR_data_analytics.html
[3] http://www.humangenomeprivacy.org/2020/
[4] https://www.mk.co.kr/today-paper/view/2020/4679442/



▶  The content is proected by law and the copyright belongs to the author.
▶  The content is prohibited to copy or quote without the author's permission.