A location‐based recommender system (LbRS) is a system which provides recommendations to a user related to his/her point of interest. In order to generate these recommendations, the LbRS uses personal information regarding the current location of the user. This creates serious privacy issues, as users' movements can be revealed through their location data. This paper proposes a new location‐based privacy protection method, and is divided into two stages. First, dummy locations are identified using query probability and distance. Second, a deep learning algorithm is trained to predict dummy locations. Then, the average entropy of each stage is used to compute final entropy. The results show that the proposed method outperforms standard methods such as random and farthest dummy location selection, although it is fractionally slower than the benchmark methods due to the encryption mechanism integrated into it to provide double‐layer security.
In recent years, privacy has become great attention in the research community. In Location-based Recommendation Systems (LbRSs), the user is constrained to build queries depend on his actual position to search for the closest points of interest (POIs). An external attacker can analyze the sent queries or track the actual position of the LbRS user to reveal his\her personal information. Consequently, ensuring high privacy protection (which is including location privacy and query privacy) is a fundamental thing. In this paper, we propose a model that guarantees high privacy protection for LbRS users. The model is work by three components: The first component (selector) uses a new location privacy protection approach, namely, the smart dummy selection (SDS) approach. The SDS approach generates a strong dummy position that has high resistance versus a semantic position attack. The second component (encryptor) uses an encryption-based approach that guarantees a high level of query privacy versus a sampling query attack. The last component (constructor) constructs the protected query that is sent to the LbRS server. Our proposed model is supported by a checkpoint technique to ensure a high availability quality attribute. Our proposed model yields competitive results compared to similar models under various privacy and performance metrics.
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