Collusion-resistant multiparty data sharing in social networks

Nisha P. Shetty, Balachandra Muniyal, Nandini Proothi, Bhavya Gopal

Research output: Contribution to journalArticlepeer-review

Abstract

The number of users on online social networks (OSNs) has grown tremendously over the past few years, with sites like Facebook amassing over a billion users. With the popularity of OSNs, the increase in privacy risk from the large volume of sensitive and private data is inevitable. While there are many features for access control for an individual user, most OSNs still need concrete mechanisms to preserve the privacy of data shared between multiple users. The proposed method uses metrics such as identity leakage (IL) and strength of interaction (SoI) to fine-tune the scenarios that use privacy risk and sharing loss to identify and resolve conflicts. In addition to conflict resolution, bot detection is also done to mitigate collusion attacks. The final decision to share the data item is then ascertained based on whether it passes the threshold condition for the above metrics.

Original languageEnglish
Pages (from-to)1996-2013
Number of pages18
JournalInternational Journal of Electrical and Computer Engineering
Volume14
Issue number2
DOIs
Publication statusPublished - 04-2024

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • Electrical and Electronic Engineering

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