Abstract
Group activities on social networks are increasing rapidly with the development of mobile devices and IoT terminals, creating a huge demand for group recommendation. However, group recommender systems are facing an important problem of privacy leakage on user’s historical data and preference. Existing solutions always pay attention to protect the historical data but ignore the privacy of preference. In this paper, we design a privacy-preserving group recommendation scheme, consisting of a personalized recommendation algorithm and a preference aggregation algorithm. With the carefully introduced local differential privacy (LDP), our personalized recommendation algorithm can protect user’s historical data in each specific group. We also propose an Intra-group transfer Privacy-preserving Preference Aggregation algorithm (IntPPA). IntPPA protects each group member’s personal preference against either the untrusted servers or other users. It could also defend long-term observation attack. We also conduct several experiments to measure the privacy-preserving effect and usability of our scheme with some closely related schemes. Experimental results on two datasets show the utility and privacy of our scheme and further illustrate its advantages.
Highlights
With the development of the social networks and the mobile devices like smartphone or IoT terminals [1, 2], recommender systems, which are used to recommend for each individual, play an important role in our daily life
The main contributions of our paper are summarized as follows: (i) We propose a privacy-aware group recommendation scheme that protects each user’s historical data and personal preferences at the same time
We use RMSE and F-score to measure the utility, and use RMSE and “matched pairs” to measure the privacy-preserving effect under long-term observation attacks
Summary
With the development of the social networks and the mobile devices like smartphone or IoT terminals [1, 2], recommender systems, which are used to recommend for each individual, play an important role in our daily life. With the latest development and popularity of smart devices and social networking services, it is convenient for people to form a group. Such trends bring new challenges to the existing recommender system: which items or events (e.g., movie/attraction/restaurant) should be recommended, in order to satisfy all/most of the group members? In 2018, about 87 million Facebook users’ information was leaked to Cambridge Analytica company By utilizing such information, the company built user model and obtain users’ personal preferences. The company built user model and obtain users’ personal preferences Based on these preferences, the company recommended targeted promotion content about the US election to these users. We should pay much attention to protect users’ privacy on recommender systems, including both of their historical data and personal preference
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