Asymmetric Social Proximity Based Community Structured Online Social Network - Gowrishetty, P; Gopi, A (International Journal of Computer Sciences and Engineering, 2020)
The extensive growth of Online Social Networks (OSNs) over the last decade has changed the way people interact with their friends, family and especially making new friends. These social networks have been an important integral part of our life. Since the count of the users of social networks is increasing drastically, so is the security threat and also private information threat. For making new friends, some OSNs work on the principle of similar profile attributes to let people become friends. However, this principle involves a privacy threat of exposing private and personal profile information to strangers over the internet. The existing solutions to secure users’ privacy are by finding the intersection of the private set of profile attributes of both the users. These schemes have few flaws and cannot hide users’ privacy. Also, in today’s online social networks any random stranger can send a friend request, check your information and misuse it too. In this paper, the community structures are used to represent the online social networks and asymmetric social proximity measure is used as people consider friendships differently. Because of social closeness measure, friend suggestions and requests originate only from relevant communities. Then, based on the asymmetric social proximity measure, a privacy algorithm is used, which provides a high level of privacy and can protect users’ privacy better than the existing works. This concept is designed using Advanced Encryption Standard Algorithm with the exchange of keys. When there is an exchange of Public and Private keys then only information can be accessed. In this paper, a secured online social network system is designed and developed as a Web application to protect the sensitive data and private information of the users and increase the security and privacy issue for OSNs. This OSN is effective and has high-level privacy protection.