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

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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.

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This research was supported by the Ministry of Science, ICT and Future Planning (MSIP), Korea, under the Information Technology Research Center (ITRC) support program (IITP-2016-H8501-16-1014) supervised by the Institute for Information and communications Technology Promotion (IITP). This work was also supported by the Dongguk University Research Fund of 2016 and the National Research Foundation of Korea’s (NRF) Basic Science Research Program, which is funded by the Ministry of Education (NRF-2016R1D1A1A09919318).

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Correspondence to Gangman Yi.

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Park, J.H., Sung, Y., Sharma, P.K. et al. Novel assessment method for accessing private data in social network security services. J Supercomput 73, 3307–3325 (2017).

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