Identifying Attack Models for Secure Recommendation
Robin Burke, Bamshad Mobasher, Roman Zabicki, Runa Bhaumik · 2004
Publicly-accessible adaptive systems such as recommender systems present a security problem. Attackers, who cannot be readily distinguished from ordinary users, may introduce biased data in an attempt to force the system to adapt in a manner advantageous to them. Recent research has begun to examine the vulnerabilities of different recommendation techniques. In this paper, we outline some of the major issues in building secure recommender systems, concentrating in particular on the modeling of attacks.