Recommender systems are widely used in a variety of scenarios, including online shopping, social network, and contents distribution. As users rely more on recommender systems for information retrieval, they also become attractive targets for cyber-attacks. The high-level idea of attacking a recommender system is straightforward. An adversary selects a strategy to inject manipulated data into the database of the recommender system to influence the recommendation results, which is also known as a profile injection attack. Most existing works treat attacking and protection in a static manner, i.e., they only consider the adversary’s behavior when analyzing the influence without considering normal users’ activities. However, most recommender systems have a large number of normal users who also add data to the database, the effects of which are largely ignored when considering the protection of a recommender system. We take normal users’ contributions into consideration and analyze popular attacks against a recommender system. We also propose a general protection framework under this dynamic setting.
Lei Xu, Lin Chen, Martin Flores, Hansheng Lei, Liyu Zhang, Mahmoud K. Quweider, Fitratullah Khan, and Weidong Shi. 2020. The Majority Rule: A General Protection on Recommender System. In Proceedings of the 1st ACM Workshop on Security and Privacy on Artificial Intelligence (SPAI '20). Association for Computing Machinery, New York, NY, USA, 40–46. DOI:https://doi.org/10.1145/3385003.3410923
SPAI '20: Proceedings of the 1st ACM Workshop on Security and Privacy on Artificial Intelligence