Cache-Based Privacy Preserving Solution for Location and Content Protection in Location-Based Services.

Location-Based Services (LBSs) are playing an increasingly important role in people's daily activities nowadays. While enjoying the convenience provided by LBSs, users may lose privacy since they report their personal information to the untrusted LBS server. Although many approaches have been proposed to preserve users' privacy, most of them just focus on the user's location privacy, but do not consider the query privacy. Moreover, many existing approaches rely heavily on a trusted third-party (TTP) server, which may suffer from a single point of failure. To solve the problems above, in this paper we propose a Cache-Based Privacy-Preserving (CBPP) solution for users in LBSs. Different from the previous approaches, the proposed CBPP solution protects location privacy and query privacy simultaneously, while avoiding the problem of TTP server by having users collaborating with each other in a mobile peer-to-peer (P2P) environment. In the CBPP solution, each user keeps a buffer in his mobile device (e.g., smartphone) to record service data and acts as a micro TTP server. When a user needs LBSs, he sends a query to his neighbors first to seek for an answer. The user only contacts the LBS server when he cannot obtain the required service data from his neighbors. In this way, the user reduces the number of queries sent to the LBS server. We argue that the fewer queries are submitted to the LBS server, the less the user's privacy is exposed. To users who have to send live queries to the LBS server, we employ the l-diversity, a powerful privacy protection definition that can guarantee the user's privacy against attackers using background knowledge, to further protect their privacy. Evaluation results show that the proposed CBPP solution can effectively protect users' location and query privacy with a lower communication cost and better quality of service.

Exploiting peer-to-peer communications for query privacy preservation in voice assistant systems.

Voice assistant system (VAS) is a popular technology for users to interact with the Internet and the Internet-of-Things devices. In the VAS, voice queries are linked to users' accounts, resulting in long-term and continuous profiling at the service provider. In this paper, we propose a VAS anonymizer aiming to mix the queries of the VAS users to increase the source anonymity. The VAS anonymizer is equipped with a pattern-matching scheme, which allows VAS devices to find effective peer relays without disclosing their query patterns. Furthermore, the VAS anonymizer is equipped with anonymity evaluation modules for evaluating real-time single query, thus reducing the risk of pattern violation at the relays. Both the requester and the relay will evaluate the real-time query based on the resulting anonymity. Only if the anonymity evaluations at both requester and relay are positive, the query will be sent to the service provider via the relay. The VAS anonymizers at VAS devices coordinate the query uploading such that the sources of the queries are anonymized, and the service provider is unable to link the voice queries to individual users. In the experiments using our customized VAS devices and the Amazon Cloud servers, the computation and communication overhead of the matching scheme is shown to be efficient, and the anonymity evaluation modules are shown to be effective in protecting the privacy of the requesters and the relays.