6533b7d1fe1ef96bd125d6f2
RESEARCH PRODUCT
Exploiting community detection to recommend privacy policies in decentralized online social networks
Andrea MichienziAndrea De SalveBarbara Guidisubject
Settore INF/01 - InformaticaExploitbusiness.industryEnd userComputer sciencePrivacy policyInternet privacy020206 networking & telecommunications02 engineering and technologyPrivacy policiesRecommender systemTheoretical Computer ScienceRecommendation systemPrivacyComputer Science0202 electrical engineering electronic engineering information engineeringSecurityDecentralized online social network020201 artificial intelligence & image processingDecentralized online social networksPrivacy policiebusinessSet (psychology)Personally identifiable informationDecentralized online social networks; Privacy; Privacy policies; Recommendation system; Securitydescription
The usage of Online Social Networks (OSNs) has become a daily activity for billions of people that share their contents and personal information with the other users. Regardless of the platform exploited to provide the OSNs’ services, these contents’ sharing could expose the OSNs’ users to a number of privacy risks if proper privacy-preserving mechanisms are not provided. Indeed, users must be able to define its own privacy policies that are exploited by the OSN to regulate access to the shared contents. To reduce such users’ privacy risks, we propose a Privacy Policies Recommended System (PPRS) that assists the users in defining their own privacy policies. Besides suggesting the most appropriate privacy policies to end users, the proposed system is able to exploits a certain set of properties (or attributes) of the users to define permissions on the shared contents. The evaluation results based on real OSN dataset show that our approach classifies users with a higher accuracy by recommending specific privacy policies for different communities of the users’ friends.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2018-12-31 |