Defending against inference attack in online social networks
Date
2017-07-19
Authors
Chen, Jiayi
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Abstract
The privacy issues in online social networks (OSNs) have been increasingly arousing the public awareness since it is possible for attackers to launch several kinds of attacks to obtain users' sensitive and private information by exploiting the massive data obtained from the networks. Even if users conceal their sensitive information, attackers can infer their secrets by studying the correlations between private and public information with background knowledge. To address these issues, the thesis focuses on the inference attack and its countermeasures.
First, we study how to launch the inference attack to profile OSN users via relationships and network characteristics. Due to both user privacy concerns and unformatted textual information, it is quite difficult to build a completely labeled social network directly. However, both social relations and network characteristics can help attribute inference to profile OSN users. We propose several attribute inference models based on these two factors and implement them with Naive Bayes, Decision Tree, and Logistic Regression. Also, to study network characteristics and evaluate the performance of our proposed models, we use a well-labeled Google employee social network extracted from Google+ for inferring the social roles of Google employees. The experiment results demonstrate that the proposed models are effective in social role inference with Dyadic Label Model performing the best.
Second, we model the general inference attack and formulate the privacy-preserving data sharing problem to defend against the attack. The optimization problem is to maximize the users' self-disclosure utility while preserving their privacy. We propose two privacy-preserving social network data sharing methods to counter the inference attack. One is the efficient privacy-preserving disclosure algorithm (EPPD) targeting the high utility, and the other is to convert the original problem into a multi-dimensional knapsack problem (d-KP) which can be solved with a low computational complexity. We use real-world social network datasets to evaluate the performance. From the results, the proposed methods achieve a better performance when compared with the existing ones.
Finally, we design a privacy protection authorization framework based on the OAuth 2.0 protocol. Many third-party services and applications have integrated the login services of popular social networking sites, such as Facebook and Google+, and acquired user information to enrich their services by requesting user's permission. However, due to the inference attack, it is still possible to infer users' secrets. Therefore, we embed our privacy-preserving data sharing algorithms in the implementation of OAuth 2.0 framework and propose RANPriv-OAuth2 to protect users' privacy from the inference attack.
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Keywords
online social network, privacy, inference attack