A Privacy-Preserving Data Collection Method for Diversified Statistical Analysis

TL;DR

This paper introduces RVNS, a novel real-value negative survey model that enables privacy-preserving collection of sensitive data distributions without discretization, enhancing data utility for diverse statistical analyses.

Contribution

The paper proposes the first real-value negative survey model, RVNS, which preserves privacy and accurately captures sensitive data distributions without requiring data discretization.

Findings

RVNS conforms to differential privacy standards.

Experimental results validate the method's effectiveness on synthetic and real data.

RVNS accurately estimates sensitive data distributions while protecting privacy.

Abstract

Data perturbation-based privacy-preserving methods have been widely adopted in various scenarios due to their efficiency and the elimination of the need for a trusted third party. However, these methods primarily focus on individual statistical indicators, neglecting the overall quality of the collected data from a distributional perspective. Consequently, they often fall short of meeting the diverse statistical analysis requirements encountered in practical data analysis. As a promising sensitive data perturbation method, negative survey methods is able to complete the task of collecting sensitive information distribution while protecting personal privacy. Yet, existing negative survey methods are primarily designed for discrete sensitive information and are inadequate for real-valued data distributions. To bridge this gap, this paper proposes a novel real-value negative survey model, termed RVNS, for the first time in the field of real-value sensitive information collection. The RVNS model exempts users from the necessity of discretizing their data and only requires them to sample a set of data from a range that deviates from their actual sensitive details, thereby preserving the privacy of their genuine information. Moreover, to accurately capture the distribution of sensitive information, an optimization problem is formulated, and a novel approach is employed to solve it. Rigorous theoretical analysis demonstrates that the RVNS model conforms to the differential privacy model, ensuring robust privacy preservation. Comprehensive experiments conducted on both synthetic and real-world datasets further validate the efficacy of the proposed method.