Group activities on social networks are increasing rapidly with the development of mobile devices and IoT terminals, creating a\nhuge demand for group recommendation. However, group recommender systems are facing an important problem of privacy\nleakage on userâ??s historical data and preference. Existing solutions always pay attention to protect the historical data but ignore\nthe privacy of preference. In this paper, we design a privacy-preserving group recommendation scheme, consisting of a\npersonalized recommendation algorithm and a preference aggregation algorithm. With the carefully introduced local differential\nprivacy (LDP), our personalized recommendation algorithm can protect userâ??s historical data in each specific group. We also\npropose an Intra-group transfer Privacy-preserving Preference Aggregation algorithm (IntPPA). IntPPA protects each group\nmemberâ??s personal preference against either the untrusted servers or other users. It could also defend long-term observation\nattack. We also conduct several experiments to measure the privacy-preserving effect and usability of our scheme with some\nclosely related schemes. Experimental results on two datasets show the utility and privacy of our scheme and further illustrate its\nadvantages.
Loading....