Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness Export

@article{citeulike:1784598, abstract = {Publicly accessible adaptive systems such as collaborative recommender systems present a security problem. Attackers, who cannot be readily distinguished from ordinary users, may inject biased profiles in an attempt to force a system to \“adapt\” in a manner advantageous to them. Such attacks may lead to a degradation of user trust in the objectivity and accuracy of the system. Recent research has begun to examine the vulnerabilities and robustness of different collaborative recommendation techniques in the face of \“profile injection\” attacks. In this article, we outline some of the major issues in building secure recommender systems, concentrating in particular on the modeling of attacks and their impact on various recommendation algorithms. We introduce several new attack models and perform extensive simulation-based evaluations to show which attacks are most successful and practical against common recommendation techniques. Our study shows that both user-based and item-based algorithms are highly vulnerable to specific attack models, but that hybrid algorithms may provide a higher degree of robustness. Using our formal characterization of attack models, we also introduce a novel classification-based approach for detecting attack profiles and evaluate its effectiveness in neutralizing attacks.}, address = {New York, NY, USA}, author = {Mobasher, Bamshad and Burke, Robin and Bhaumik, Runa and Williams, Chad}, citeulike-article-id = {1784598}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1278372}, citeulike-linkout-1 = {http://dblp.uni-trier.de/rec/bibtex/journals/toit/MobasherBBW07}, citeulike-linkout-2 = {http://dx.doi.org/10.1145/1278366.1278372}, doi = {10.1145/1278366.1278372}, issn = {1533-5399}, journal = {ACM Trans. Internet Technol.}, keywords = {recommender, systems, trustworthy}, month = {October}, number = {4}, pages = {23+}, posted-at = {2007-10-23 09:11:33}, priority = {4}, publisher = {ACM}, title = {Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness}, url = {http://dx.doi.org/10.1145/1278366.1278372}, volume = {7}, year = {2007} }

TY - JOUR ID - citeulike:1784598 L3 - citeulike-article-id:1784598 N2 - Publicly accessible adaptive systems such as collaborative recommender systems present a security problem. Attackers, who cannot be readily distinguished from ordinary users, may inject biased profiles in an attempt to force a system to “adapt” in a manner advantageous to them. Such attacks may lead to a degradation of user trust in the objectivity and accuracy of the system. Recent research has begun to examine the vulnerabilities and robustness of different collaborative recommendation techniques in the face of “profile injection” attacks. In this article, we outline some of the major issues in building secure recommender systems, concentrating in particular on the modeling of attacks and their impact on various recommendation algorithms. We introduce several new attack models and perform extensive simulation-based evaluations to show which attacks are most successful and practical against common recommendation techniques. Our study shows that both user-based and item-based algorithms are highly vulnerable to specific attack models, but that hybrid algorithms may provide a higher degree of robustness. Using our formal characterization of attack models, we also introduce a novel classification-based approach for detecting attack profiles and evaluate its effectiveness in neutralizing attacks. IS - 4 JF - ACM Trans. Internet Technol. SN - 1533-5399 AD - New York, NY, USA TI - Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness PB - ACM VL - 7 SP - 23 KW - recommender KW - systems KW - trustworthy AU - Mobasher, Bamshad AU - Burke, Robin AU - Bhaumik, Runa AU - Williams, Chad PY - 2007/10// UR - http://dx.doi.org/10.1145/1278366.1278372 ER -