Novel assessment method for accessing private data in social network security services

Jong Hyuk Park, Yunsick Sung, Pradip Kumar Sharma, Young Sik Jeong, Gangman Yi*

*Corresponding author for this work

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Social network services (SNSs) have become one of the core Internet-based application services in recent years. Through SNSs, diverse kinds of private data are shared with users’ friends and SNS plug-in applications. However, these data can be exposed via abnormal private data access. For example, the addition of fake friends to a user’s account is one approach to gain access to a private user’s data. Private user data can be protected from being accessed by using an automated method to assess information. This paper proposes a method that evaluates private data accesses for social network security. By defining normal private data access patterns in advance, abnormal private data access patterns can be exposed. Normal private data access patterns are generated by analyzing all of the consecutive private data accesses of users based on Bayesian probability. We have proven the effectiveness of our approach by conducting experiments where the private data access signals of Twitter accounts were collected and analyzed.

Original languageEnglish
Pages (from-to)3307-3325
Number of pages19
JournalJournal of Supercomputing
Volume73
Issue number7
Early online date20 Mar 2017
DOIs
Publication statusPublished - 2017

Keywords

  • Security and privacy protection
  • Social networking
  • Unauthorized access

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