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Title Page

Abstract

Contents

1. Introduction 7

2. Related work 9

3. Preliminaries 10

3.1. Differential Privacy 10

3.2. Homomorphic Encryption 11

3.3. Preconditioned Stochastic Gradient Langevin Dynamics 12

4. Proposed Approach 13

4.1. Privacy Analysis for pSGLD with RMSprop 13

4.2. Secure and Differentially Private Distributed Bayesian Learning 15

4.2.1. Precomputation Phase 15

4.2.2. Iterative Model Estimation Phase 20

4.2.3. Threat Model 23

5. Experiments 24

5.1. Experimental Results 24

5.1.1. Bayesian density estimation with simple simulation 25

5.1.2. Bayesian Logistic Regression 25

5.1.3. Bayesian Survival Analysis 27

5.2. Performance Analysis 28

6. Conclusion 29

Reference 30

초록보기

The performance of Machine learning (ML) depends on quantity of data. In real-world, however, data is collected by multiple institutions. Data integration and sharing maximally enhance the potential for novel and meaningful discoveries. However, it is a non-trivial task as integrating data from multiple sources can put sensitive information of study participants at risk. To address the privacy concern, we present a distributed Bayesian learning approach via Preconditioned Stochastic Gradient Langevin Dynamics with RMSprop, which combines differential privacy and homomorphic encryption in a harmonious manner while protecting private information. We applied the proposed secure and privacy-preserving distributed Bayesian learning approach to logistic regression and survival analysis on distributed data, and demonstrated its feasibility in terms of prediction accuracy and time complexity, compared to the centralized approach.

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