TY - GEN
T1 - The Majority Rule
T2 - 1st ACM Workshop on Security and Privacy on Artificial Intelligent, SPAI 2020, Co-located with AsiaCCS 2020
AU - Xu, Lei
AU - Chen, Lin
AU - Flores, Martin
AU - Lei, Hansheng
AU - Zhang, Liyu
AU - Quweider, Mahmoud K.
AU - Khan, Fitratullah
AU - Shi, Weidong
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/10/6
Y1 - 2020/10/6
N2 - 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.
AB - 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.
KW - protection
KW - recommender system
KW - vrf
UR - http://www.scopus.com/inward/record.url?scp=85095996237&partnerID=8YFLogxK
U2 - 10.1145/3385003.3410923
DO - 10.1145/3385003.3410923
M3 - Conference contribution
AN - SCOPUS:85095996237
T3 - SPAI 2020 - Proceedings of the 1st ACM Workshop on Security and Privacy on Artificial Intelligent, Co-located with AsiaCCS 2020
SP - 40
EP - 46
BT - SPAI 2020 - Proceedings of the 1st ACM Workshop on Security and Privacy on Artificial Intelligent, Co-located with AsiaCCS 2020
PB - Association for Computing Machinery, Inc
Y2 - 6 October 2020
ER -