Differentially private data release and analytics Open Access

Li, Haoran (Fall 2017)

Permanent URL: https://etd.library.emory.edu/concern/etds/pr76f341v?locale=en
Published

Abstract

Nowadays data sharing is important for application domains, such as scientific discoveries, business strategies, commercial interests, and social goods, especially when there are not enough local samples to test a hypothesis. However, data in its raw format are sensitive as they essentially contain individual specific information, and publishing such data without proper protection may disclose personal privacy. Netflix canceled their recommendation system contest because the released customers data can identify special individuals with high probability. In order to promote data sharing, it is important to develop privacy-preserving algorithms that respect data confidentiality while present data utility. In this dissertation, we address the privacy concerns in publishing highdimensional static data and dynamic datasets, and developing mechanisms for personalized differential privacy where data subjects can have various privacy preferences. Our privacy preserving algorithms satisfy differential privacy, a rigorous and de facto standard for privacy protection. Extensive empirical studies demonstrate the effectiveness of our solutions and confirm that our methods have great promise for privacy-preserving data release and analytical tasks in a wide range of applicationdomains.

Table of Contents

Contents

List of Figures

List of Tables

Chapter 1. Introduction

1.1. Motivation

1.2. Research Contributions

Chapter 2. Related Works

2.1. Differentially private synthetic data generation

2.2. Differentially private dynamic data generation

2.3. Personalized dierential privacy

Chapter 3. Differentially private synthesization of multi-dimensional data using

copula functions

3.1. Preliminaries

3.2. DPCopula

3.3. Experiment

Chapter 4. Privacy-preserving dynamic histogram release with distance-based sampling

4.1. Preliminaries

4.2. Adaptive Sampling Approach

4.3. Utility Analysis

4.4. Extensions to innite streams

4.5. Experiment

4.6. Conclusions

Chapter 5. Personalized differential privacy

5.1. Preliminaries

5.2. Partitioning mechanisms

5.3. Experiment

Chapter 6. Conclusions

6.1. Summary of Dissertation

6.2. Recommendations for Future Work

Bibliography

About this Dissertation

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