2016/05/20 by Silvia Puglisi, Javier Parra‐Arnau, Puglisi, Silvia +5
Computer Science · Social Sciences · #Computers and Society (cs.CY) #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Privacy, Security, and Data Protection #Privacy-Preserving Technologies in Data #Recommender Systems and Techniques
paper · pdf · doi:10.48550/arxiv.1605.06538
openalex publication_date 2016/05/20 · openalex created_date 2022/10/03 · openalex updated_date 2026/07/28
Recommendation systems and content filtering approaches based on annotations\nand ratings, essentially rely on users expressing their preferences and\ninterests through their actions, in order to provide personalised content. This\nactivity, in which users engage collectively, has been named social tagging.\nAlthough it has opened a myriad of new possibilities for application\ninteroperability on the semantic web, it is also posing new privacy threats.\nSocial tagging consists in describing online or online resources by using\nfree-text labels (i.e. tags), therefore exposing the user's profile and\nactivity to privacy attacks. Tag forgery is a privacy enhancing technology\nconsisting of generating tags for categories or resources that do not reflect\nthe user's actual preferences. By modifying their profile, tag forgery may have\na negative impact on the quality of the recommendation system, thus protecting\nuser privacy to a certain extent but at the expenses of utility loss. The\nimpact of tag forgery on content-based recommendation is, therefore,\ninvestigated in a real-world application scenario where different forgery\nstrategies are evaluated, and the consequent loss in utility is measured and\ncompared.\n