Collection and Analysis of Sensitive Data with Privacy Protection by a Distributed Randomized Response Protocol
Faisal Imran, Rosa Meo · 2024
The data collected from personal devices is intrinsically private and should be collected through a privacy-guaranteed mechanism. Local differential privacy solves privacy problems by collecting randomized responses from each user, and it does not need to rely on a trusted data aggregator/curator. The proposed approach utilizes the randomized response technique in a novel manner: it guarantees privacy to users during the data collection and simultaneously preserves the high utility of the analysis. It can be seen as a case of synthetic data generation by producing contingency tables (marginals) in a privacy-preserving mechanism. This article describes the proposed randomized response technique and discusses the motivating applications domains. It justifies why it satisfies the property of differential privacy and utility guarantees theoretically and through experimental analysis with excellent results.