Social Networking for Data Preservation: A Study

Objectives: This paper provides an overview of social network, privacy preservation in social networking, using kanonymity and L-diversity. Methods/Statistical Analysis: K-anonymity, L-diversity is used for social networking. In k-anonymity privacy micro data requires that each class contains at least K records. But k anonymity doesn't prevent attribute disclosure. To solve this I-diversity has been used. Practically L-diversity has been used and can be implemented efficiently. Findings: As privacy is the major concern in online social networking, so research in this field is going continuously. Methods like tabular micro data has been proposed by number of authors that provides solutions for privacy concern. But this method cannot be applied directly because social networking consists of number of edges and nodes. To provide better privacy and security for social networking L-diversity and K-diversity are used in combination. Improvement is done by using t-closeness technique. Many applications needs to publish data in binary form so there is a need to develop techniques that can preserve privacy of dynamic release.