Social Anchor: Privacy-Friendly Attribute Aggregation From Social Networks

• Main Authors: Md. Sadek Ferdous, Farida Chowdhury, Madini O. Alassafi, Abdulrahman A. Alshdadi, Victor Chang
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Attribute aggregation; privacy; social networks; SAML; OpenID; OAuth
• Online Access: https://ieeexplore.ieee.org/document/9039639/

In the last decade or so, we have experienced a tremendous proliferation and popularity of different Social Networks (SNs), resulting more and more user attributes being stored in such SNs. These attributes represent a valuable asset and many innovative online services are offered in exchange of such attributes. This particular phenomenon has allured these social networks to act as Identity Providers (IdPs). However, the current setting unnecessarily imposes a restriction: a user can only release attributes from one single IdP in a single session, thereby, limiting the user to aggregate attributes from multiple IdPs within the same session. In addition, our analysis suggests that the manner by which attributes are released from these SNs is extremely privacy-invasive and a user has very limited control to exercise her privacy during this process. In this article, we present Social Anchor, a system for attribute aggregation from social networks in a privacy-friendly fashion. Our proposed Social Anchor system effectively addresses both of these serious issues. Apart from the proposal, we have implemented Social Anchor following a set of security and privacy requirements. We have also examined the associated trust issues using a formal trust analysis model. Besides, we have presented a formal analysis of its protocols using a state-of-the-art formal analysis tool called AVISPA to ensure the security of Social Anchor. Finally, we have provided a performance analysis of Social Anchor.