In this paper, we propose a privacy-preserving approach to prevent feature disclosure in a multiple IoT scenario, i.e., a scenario where objects can be organized in (partially overlapped) networks interacting with each other. Our approach is based on two notions derived from database theory, namely k-anonymity and t-closeness. They are applied to cluster the involved objects in order to provide a unitary view of them and of their features. Indeed, the use of k-anonymity and t-closeness makes derived groups robust from a privacy perspective. In this way, not only information disclosure, but also feature disclosure, is prevented. This is an important strength of our approach because the malicious analysis of objects’ features can have disruptive effects on the privacy (and, ultimately, on the life) of people.
By Serena Nicolazzoa, Antonino Nocerab, Domenico Ursinoc, and Luca Virgilic, 2019