During the past decade Online Social Networks (OSN) privacy has been thoroughly studied in many aspects. Some of these privacy related aspects are trust and credibility involving the OSN user-data conveyed by different relationships in the network. One of OSN major problems is that users expose their information in a manner thought to be relatively private, or even partially public, to unknown and possibly unwanted entities, such as adversaries, social bots, fake users, spammers or data-harvesters. That is one of the reasons OSN have become a major source of information for companies, different organizations and personal users, possibly misusing it for personal or business gain. Preventing this information leakage is the target of many OSN privacy models, such as Access Control, Relationship based models, Trust based models and many others. In this paper we suggest a new Role and Trust based Access Control model, denoted here as RTBAC, in which roles, that manifest different permissions, are assigned to the users connected to the Ego-node (the user sharing the information), and in addition, every user is evaluated trust wise by several criteria, such as total number of friends, age of user account, and friendship duration. These role and trust assessments provide more precise and viable information sharing decisions and enable better privacy control in the social network.
|Title of host publication||Cyber Security Cryptography and Machine Learning - 3rd International Symposium, CSCML 2019, Proceedings|
|Editors||Shlomi Dolev, Danny Hendler, Sachin Lodha, Moti Yung|
|Number of pages||13|
|State||Published - 2019|
|Event||3rd International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2019 - Beer Sheva, Israel|
Duration: 27 Jun 2019 → 28 Jun 2019
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||3rd International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2019|
|Period||27/06/19 → 28/06/19|
Bibliographical notePublisher Copyright:
© Springer Nature Switzerland AG 2019.
- Access control
- Online Social Networks Privacy
- Trust-based privacy models