Collection and Analysis of Sensitive Data with Privacy Protection by a Distributed Randomized Response Protocol

The data collected from smart devices, the Internet of Things (IoT), and Smart Homes can be used for mining purposes and potentially benefit organizations with a large user base. 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. It allows for building reliable prediction models on the collected amount of randomized data. 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.