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Synergizing Privacy and Utility in Data Analytics Through Advanced Information Theorization

Zahir Alsulaimawi

TL;DR

The paper tackles the problem of balancing data utility and privacy in analytics by casting privacy-utility as a mutual-information optimization and proposing three algorithms: Noise-Infusion for high-dimensional data, a Variational Autoencoder (VAE) approach, and an EM-based method. It contributes theoretical results, including information bounds and convergence guarantees for alternating optimization, and a practical framework integrating variational inference with EM to improve optimization. Empirical validation on Modified MNIST, CelebrityA, and a structured dataset demonstrates superior privacy protection with retained utility relative to basic privacy techniques. The work offers a flexible, theory-grounded pathway for deploying privacy-preserving analytics across diverse data types and applications.

Abstract

This study develops a novel framework for privacy-preserving data analytics, addressing the critical challenge of balancing data utility with privacy concerns. We introduce three sophisticated algorithms: a Noise-Infusion Technique tailored for high-dimensional image data, a Variational Autoencoder (VAE) for robust feature extraction while masking sensitive attributes and an Expectation Maximization (EM) approach optimized for structured data privacy. Applied to datasets such as Modified MNIST and CelebrityA, our methods significantly reduce mutual information between sensitive attributes and transformed data, thereby enhancing privacy. Our experimental results confirm that these approaches achieve superior privacy protection and retain high utility, making them viable for practical applications where both aspects are crucial. The research contributes to the field by providing a flexible and effective strategy for deploying privacy-preserving algorithms across various data types and establishing new benchmarks for utility and confidentiality in data analytics.

Synergizing Privacy and Utility in Data Analytics Through Advanced Information Theorization

TL;DR

The paper tackles the problem of balancing data utility and privacy in analytics by casting privacy-utility as a mutual-information optimization and proposing three algorithms: Noise-Infusion for high-dimensional data, a Variational Autoencoder (VAE) approach, and an EM-based method. It contributes theoretical results, including information bounds and convergence guarantees for alternating optimization, and a practical framework integrating variational inference with EM to improve optimization. Empirical validation on Modified MNIST, CelebrityA, and a structured dataset demonstrates superior privacy protection with retained utility relative to basic privacy techniques. The work offers a flexible, theory-grounded pathway for deploying privacy-preserving analytics across diverse data types and applications.

Abstract

This study develops a novel framework for privacy-preserving data analytics, addressing the critical challenge of balancing data utility with privacy concerns. We introduce three sophisticated algorithms: a Noise-Infusion Technique tailored for high-dimensional image data, a Variational Autoencoder (VAE) for robust feature extraction while masking sensitive attributes and an Expectation Maximization (EM) approach optimized for structured data privacy. Applied to datasets such as Modified MNIST and CelebrityA, our methods significantly reduce mutual information between sensitive attributes and transformed data, thereby enhancing privacy. Our experimental results confirm that these approaches achieve superior privacy protection and retain high utility, making them viable for practical applications where both aspects are crucial. The research contributes to the field by providing a flexible and effective strategy for deploying privacy-preserving algorithms across various data types and establishing new benchmarks for utility and confidentiality in data analytics.
Paper Structure (83 sections, 7 theorems, 31 equations, 2 figures, 3 tables, 3 algorithms)

This paper contains 83 sections, 7 theorems, 31 equations, 2 figures, 3 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Concepts and Preliminaries
  4. Challenge of Balancing Utility and Privacy
  5. Articulation of the Optimization Dichotomy
  6. Mathematical Formulation of the Optimization Paradigm
  7. Theoretical Insights: Foundations and Applications
  8. The Importance of Theoretical Constructs
  9. Tractability through Information Bounds
  10. Synthesizing the Bounds
  11. Advanced Theoretical Underpinnings of Variational Information Optimization
  12. Ensuring Convergence with Alternating Optimization Strategies
  13. Optimizing Utility and Privacy: An Algorithmic Approach
  14. Algorithm Outline
  15. Neural Estimators for Mutual Information with Binary Labels and High-Dimensional Images
  16. ...and 68 more sections

Key Result

Theorem 1

Consider discrete random variables $X$, $U$, and $S$ with a joint probability distribution $p(x, u, s)$. By delineating lower and upper bounds on the mutual information terms $I(Y; U)$ and $I(Y; S)$ within the objective function we facilitate a refined approximation of the complex optimization problem, enabling a more effective analytical and computational approach to balancing data utility again

Figures (2)

  • Figure 1: Reduction in Mutual Information with Increasing Noise Levels
  • Figure 2: Utility Loss vs. Privacy Gain with Varying Noise Levels

Theorems & Definitions (14)

  • Theorem 1: Enhanced Tractability through Information Bounds
  • proof
  • Theorem 2: Sophisticated Lower Bound on Mutual Information
  • proof
  • Theorem 3: Convergence of Alternating Optimization Techniques
  • proof
  • Theorem 4: Stability and Convergence of the EM Algorithm
  • proof
  • Lemma 5: Sensitivity Analysis of the EM Algorithm
  • proof
  • ...and 4 more