Ning Gu

TL;DR: An efficient privacy-preserving item-based collaborative filtering algorithm is proposed, which can protect user privacy during online recommendation process without compromising recommendation accuracy and efficiency.

TL;DR: This paper provides the proof of the algorithm's correctness of consistency maintenance, in which both the orders of character nodes and marks of each node at all sites are kept consistent, and the amortized efficiency can reach O(log n).

TL;DR: E evaluation results gathered from real-world user studies demonstrate that the proposed Farseer system outperforms three widely-used collaborative filtering algorithms (kNN, PLSA, SVD) in existing recommender systems and can effectively identify personal interests and improve the quality and efficiency of real-time personalized content recommendation in online social communities.

TL;DR: It is shown that making and hacking is not practiced in a void independent of social, political or economic forces, Rather, making unfolds in relation to, and is contingent on, societal norms and specific techno-cultural histories.

TL;DR: YANA (short for "you are not alone"), a user group-based privacy-preserving recommender system for users in online social communities, is proposed, which is a suit of secure multi-party computation protocols and recommendation strategies proposed to protect user privacy from group members in the recommendation process.