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Approximating Functions with Approximate Privacy for Applications in Signal Estimation and Learning.

Naima Tasnim¹, Jafar Mohammadi², Anand D Sarwate³

¹Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka P.O. Box 1205, Bangladesh.

Entropy (Basel, Switzerland)

|May 27, 2023

Summary

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This summary is machine-generated.

We introduce Gaussian FM, a new algorithm improving functional mechanisms for better privacy-utility trade-offs in data analysis. This method offers significantly reduced noise, enhancing utility while maintaining approximate differential privacy (DP).

Area of Science:

Computer Science
Data Privacy
Algorithm Design

Background:

Organizations collect sensitive personal data, necessitating privacy-preserving algorithms.
Differential privacy (DP) offers rigorous privacy but often compromises data utility.
Existing functional mechanisms (FM) face a utility-cost trade-off under DP.

Purpose of the Study:

To develop an improved functional mechanism (FM) offering enhanced utility with approximate differential privacy (DP).
To reduce the noise in DP algorithms, thereby improving the privacy-utility balance.
To extend the proposed mechanism to decentralized data settings.

Main Methods:

Proposed Gaussian FM, an enhancement to the functional mechanism (FM) using Gaussian noise.
Developed capeFM by integrating Gaussian FM with the CAPE protocol for decentralized data.

Keywords:

decentralized-data systems differential privacy functional mechanism

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Analyzed noise reduction and utility improvements analytically and empirically.

Main Results:

Gaussian FM offers orders of magnitude less noise compared to existing FM algorithms.
capeFM achieves utility comparable to centralized methods in decentralized settings.
Empirical results demonstrate superior performance over state-of-the-art methods on various datasets.

Conclusions:

Gaussian FM provides a superior privacy-utility trade-off for data analysis.
The proposed methods enhance data utility while preserving individual privacy.
These algorithms represent a significant advancement in privacy-preserving data analysis techniques.