Asymmetric Social Proximity Based Private Matching Protocols for Online Social Networks

Abstract

Online social networks (OSNs) have accomplished excellent growth in recent years and develop into a de
facto portal for hundreds of millions of Internet users. These OSNs offer attractive means for digital social
cooperation and information sharing, but also raise a number of security and privacy problem. While OSNs allow
users to restrict access to shared data, they currently do not provide any mechanism to accomplish privacy concerns
over data associated with multiple users. Some functions propose to allow individual people turn into buddies if they
have equivalent profile attributes. Evan so, profile matching requires an inherent privacy of exposing personal profile
to strangers in the cyberspace. The current answers to the difficulty try to defend end users’ privacy by privately
computing the intersection or intersection cardinality of the profile attribute sets of two end users. These schemes
have some limitations and can even now reveal end users’ privacy. In this paper, we leverage neighborhood structures
to redefine the OSN model and propose a reasonable asymmetric social proximity measure in between two end users.
Then, primarily based on the proposed asymmetric social proximity, we design and style two personal matching
protocols, which offer various privacy ranges and can defend end users’ privacy much better than the earlier
|functions. We also analyze the computation and communication expense of these protocols. Ultimately, we validate
our proposed protocols measure utilizing actual social network information and perform extensive in depth
simulations to assess the overall performance of the proposed protocols in terms of computation expense
communication expense complete operating time, and power consumption.