Abstract
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.
Highlights
Cyberphysical systems (CPS), which deeply integrate different computing, communication, controlling, and monitoring components, have leveraged modern services in our daily life, like smart grid, intelligent transportation, automatic driving, etc
Recent development of mobile communication and networks has leveraged many modern applications built on interactive cyberphysical systems, in which client software programs or devices take actions according to their interactions with CPS servers
The reason is that Multilayer Alignment (MLA) splits larger prior probability of query contents into a larger number of reports; it is more suitable to deal with skewed prior distribution of query contents
Summary
Cyberphysical systems (CPS), which deeply integrate different computing, communication, controlling, and monitoring components, have leveraged modern services in our daily life, like smart grid, intelligent transportation, automatic driving, etc. When the CPS server holds certain side information such as the prior probability of query contents, cloaking based solutions will suffer from further privacy breach. To guarantee the utility of requests to the CPS servers, we adopt k-anonymity in order to prevent the adversary from recognizing the actual query content from the k reported contents, since the actual query content must be sent to the CPS server for a meaningful reply In this process, the major challenge arises in two aspects. We theoretically introduce the properties of MLA by proving that MLA achieves the optimal expected entropy and the optimal dp-ratio at the same time These attractive properties make MLA the optimal k-anonymity solution for preserving content privacy in interactive cyberphysical systems.
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