A SURVEY ON PRIVACY PRESERVING TECHNIQUES FOR SOCIAL NETWORK DATA

In this era of 20th century, online social network like Facebook, twitter, etc. plays a very important role in everyone's life. Social network data, regarding any individual organization can be published online at any time, in which there is a risk of information leakage of anyone's personal data. So preserving the privacy of individual organizations and companies are needed before data is published online. Therefore the research was carried out in this area for many years and it is still going on. There have been various existing techniques that provide the solutions for preserving privacy to tabular data called as relational data and also social network data represented in graphs. Different techniques exists for tabular data but you can't apply directly to the structured complex graph  data,which consists of vertices represented as individuals and edges representing some kind of connection or relationship between the nodes. Various techniques like K-anonymity, L-diversity, and T-closeness exist to provide privacy to nodes and techniques like edge perturbation, edge randomization are there to provide privacy to edges in social graphs. Development of new techniques by  Integration to exiting techniques like K-anonymity ,edge perturbation, edge randomization, L-Diversity are still going on to provide more privacy to relational data and social network data are ongoingin the best possible manner.Â

A privacy-preserving model to control social interaction behaviors in social network sites

Social Network Sites (SNSs) served as an invaluable platform to transfer information across a large number of users. SNSs also disseminate users data to third-parties to provide more interesting services for users as well as gaining profits. Users grant access to third-parties to use their services, although they do not necessarily protect users’ data privacy. Controlling social network data diffusion among users and third-parties is difficult due to the vast amount of data. Hence, undesirable users’ data diffusion to unauthorized parties in SNSs may endanger users’ privacy. This paper highlights the privacy breaches on SNSs and emphasizes the most significant privacy issues to users. The goals of this paper are to i) propose a privacy-preserving model for social interactions among users and third-parties; ii) enhance users’ privacy by providing access to the data for appropriate third-parties. These advocate to not compromising the advantages of SNSs information sharing functionalities

Privacy Enhancing Technologies (PET) and web-based social networks (WBSN)

The technological threatens to the right of privacy are not only limited to databases. WBSN and pervasive computer, for instance, are two clear examples of other privacy risks. WBSN have an economic value, and more and more tools focus on WBSN users' personal information. On the contrary, WBSN privacy is only a new research area. Internet communities are trust-based systems. Therefore, they need a privacy-respecting reputation system. Transparency tools should also allow individuals to check at any desired moment what personal data has been given to the data systems, and be able to alter or delete it. IT researchers usually consider privacy as a quantifiable attribute that can be negotiated and possibly exchanged by individuals in return for certain benefits. On the contrary, PET are necessary in WBSN. Thus, they cannot simply be individual options. Human rights, as public policies, should be preserved in the design of IT tools