Plausible deniability for privacy-preserving data synthesis

TL;DR

This paper introduces a data synthesis scheme based on plausible deniability to protect high-dimensional data privacy, addressing the limitations of existing differential privacy methods in terms of computational efficiency and data accuracy.

Contribution

It proposes a novel privacy protection scheme using plausible deniability, with theoretical support and a modular design for data synthesis and privacy testing.

Findings

Effective privacy protection demonstrated on US census data

Scheme achieves high efficiency and reliability in high-dimensional data

Outperforms traditional differential privacy methods in accuracy

Abstract

In the field of privacy protection, publishing complete data (especially high-dimensional data sets) is one of the most challenging problems. The common encryption technology can not deal with the attacker to take differential attack to obtain sensitive information, while the existing differential privacy protection algorithm model takes a long time for high-dimensional calculation and needs to add noise to reduce data accuracy, which is not suitable for high-dimensional large data sets. In view of this situation, this paper designs a complete data synthesis scheme to protect data privacy around the concept of "plausible denial". Firstly, the paper provides the theoretical support for the difference between "plausible data" and "plausible data". In the process of scheme designing, this paper decomposes the scheme design into construction data synthesis module and privacy test module, then designs algorithm models for them respectively and realizes the function of privacy protection. When evaluating the feasibility of the scheme, the paper selects the Results of the 2013 community census in the United States as the high-dimensional data set, uses the simulation program that is based on Python to test and analyzes the efficiency and reliability of the data synthesis scheme. This portion focuses on the evaluation of the privacy protection effectiveness of the scheme.