Achieving the Optimal k-Anonymity for Content Privacy in Interactive Cyberphysical Systems

Modern applications and services leveraged by interactive cyberphysical systems (CPS) are providing significant convenience to our daily life in various aspects at present. Clients submit their requests including query contents to CPS servers to enjoy diverse services such as health care, automatic driving, and location-based services. However, privacy concerns arise at the same time. Content privacy is recognized and a lot of efforts have been made in the literature of privacy preserving in interactive cyberphysical systems such as location-based services. Nevertheless, neither the cloaking based solutions nor existing client based solutions have achieved effective content privacy by optimizing proper content privacy metrics. In this paper we formulate the problem of achieving the optimal content privacy in interactive cyberphysical systems using k-anonymity solutions based on two content privacy metrics, which are defined using the concepts of entropy and differential privacy. Then we propose an algorithm, Multilayer Alignment (MLA), to establish k-anonymity mechanisms for preserving content privacy in interactive cyberphysical systems. Our proposed MLA is theoretically proved to achieve the optimal content privacy in terms of both the entropy based and the differential privacy mannered content privacy metrics. Evaluation based on real-life datasets is conducted, and the evaluation results validate the effectiveness of our proposed algorithm.