Abstract
With the rapid development of location based social networks (LBSN) and location based services (LBS), the location recommendation to users has gained much attentions. A traditional location recommendation scheme may use any of the following information to generate a location recommendation: users’ check-in frequencies on different locations, their distance of other locations from any point of interest (POI), time of visiting different locations, social influence or interests on those locations which are visited by friends and so on. Depending on different contexts and tastes, results of recommending new location may vary. Again the users might have specific preferences of context to find the most suitable locations for him. However, these contextual information and preferences related to users are personal and an user usually does not want to reveal these information to any third party which are the main source of information to generate a recommendation. Revealing these information may cause to misuse or expose the data to third parties which is clearly breaching privacy of users. In this circumstances, it is essential to hide users’ check-in history in different locations from service providers, and get advantages of the server’s processing power to generate user personalized location recommendations. To address these challenges we present a cryptographic framework to preserve users’ privacy and simultaneously generating location recommendations for users. We also incorporate users’ friendship network along with the location preferences and show that users are able to choose their friends’ preferences on different locations to influence the recommendation results without revealing any information. The security and performance analysis show that the protocol is secure as well as practical.
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Notes
- 1.
In the Paillier cryptosystem, encryption of a negative integer can be calculated by using its modular additive inverse, i.e. \(E(x) = E(nx)\), so we can treat all \(x>\frac{n}{2}\) to be negative.
- 2.
In this research we only focus on preserving check-in frequency on different location. We leave preserving users’ visit on different locations as future work.
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Badsha, S., Yi, X., Khalil, I., Liu, D., Nepal, S., Bertino, E. (2017). Privacy Preserving Location Recommendations. In: Bouguettaya, A., et al. Web Information Systems Engineering – WISE 2017. WISE 2017. Lecture Notes in Computer Science(), vol 10570. Springer, Cham. https://doi.org/10.1007/978-3-319-68786-5_40
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DOI: https://doi.org/10.1007/978-3-319-68786-5_40
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