open access

Trust Based Allocating And Sharing Resources Via Social Networks With Multiparty Secured Access Control

  • Kavin kumar D Student,Department of CSE, K.S.R College of Engineering, Tiruchengode, Tamilnadu, India
  • Kavipriya A Student,Department of CSE, K.S.R College of Engineering, Tiruchengode, Tamilnadu, India
  • Logeshwaran J Student,Department of CSE, K.S.R College of Engineering, Tiruchengode, Tamilnadu, India
  • Nandhakumar E Student,Department of CSE, K.S.R College of Engineering, Tiruchengode, Tamilnadu, India
  • Umamaheswari M Assistant Professor,Department of CSE, K.S.R College of Engineering, Tiruchengode, Tamilnadu, India

Abstract

Online social networks (OSNs) have experienced tremendous growth in recent years. These OSNs offer attractive means for digital social interactions and information sharing, but also raise a number of security and privacy issues. While OSNs allow users to restrict access to shared data, they currently do not provide any mechanism to enforce privacy concerns over data associated with multiple users. This paper enhances existing and introduces new social network privacy management models and measures their human effects. First, it introduces a mechanism using proven clustering techniques that assists users in grouping their friends for traditional group-based policy management approaches. It found measurable agreement between clusters and user-defined relationship groups. Second, it introduces a new privacy management model that leverages users’ memory and opinion of their friends (called example friends) to set policies for other similar friends. Finally, it explores different techniques that aid users in selecting example friends. It is found that by associating policy temples with example friends (versus group labels), users author policies more efficiently and have improved perceptions over traditional group-based policy management approaches. In addition, the results show that privacy management models can be further enhanced by utilizing user privacy sentiment for mass customization. By detecting user privacy sentiment (i.e., an unconcerned user, a pragmatist or a fundamentalist), privacy management models can be automatically tailored specific to the privacy sentiment and needs of the user.

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