Ayano Yoshikuni
ABSTRACT
With the increased worldwide popularity of social networking services (SNSs), the leakage of a user's private information is becoming a serious problem. An increased number of users now have multiple accounts on various social networks and they tend to use each account to write different user experiments. Aggregating information from different accounts leads to the unintended leakage of personal information. Therefore, we argue that SNS users should be vigilant in protecting the relationship between multiple accounts.
In this paper, we propose the use of Account Reachability, a measure of privacy risk which demonstrates the possibility of a stranger finding a user's private account based on information in their public account. In addition, we present ARChecker, a tool to calculate the value of account reachability. ARChecker also provides advice on how to modify the user's profiles and messages to decrease the privacy risk. By checking the privacy measure and modifying the profiles and messages of their SNS accounts, users can protect their multiple accounts from the risk of an unintended leakage of personal information.
- Connecting the Social Graph: Member Overlap at OpenSocial and Facebook. https://blog.compete.com/2007/11/12/connecting-the-social-graph-member-overlap-at-opeusocial-and-facebook/.Google Scholar
- Multiple Personalities And Social Media: The Many Faces of Me. http://www.forbes.com/sites/meghaucasserly/2011/01/26/multiple-personalities-and-social-media-the-many-faces-of-me/.Google Scholar
- C. G. Akcora, B. Carminati, and E. Ferrari. Privacy in social networks: How risky is your social graph? In ICDE, pages 9--19. IEEE Computer Society, 2012. Google ScholarDigital Library
- D. Boyd and E. Hargittai. Facebook privacy settings: Who cares? First Monday, 15(8), 2010.Google Scholar
- B. Carminati, E. Ferrari, S. Morasca, and D. Taibi. A probability-based approach to modeling the risk of unauthorized propagation of information in on-line social networks. In Proceedings of the first ACM conference on Data and application security and privacy, CODASPY '11, pages 51--62. ACM, 2011. Google ScholarDigital Library
- A. Dubrawski, P. Sarkar, and L. Chen. Trade-offs between agility and reliability of predictions in dynamic social networks used to model risk of microbial contamination of food. In Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining, ASONAM '09, pages 125--130. IEEE Computer Society, 2009. Google ScholarDigital Library
- D. Irani, S. Webb, C. Pu, and K. Li. Modeling unintended personal-information leak age from multiple online social networks. IEEE Internet Computing, 15(3):13--19, 2011. Google ScholarDigital Library
- A. N. Joinson. Looking at, looking up or keeping up with people?: motives and use of facebook. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI '08, pages 1027--1036. ACM, 2008. Google ScholarDigital Library
- R. Li, S. Wang, H. Deng, R. Wang, and K. C.-C. Chang. Towards social user profiling: unified and discriminative influence model for inferring home locations. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '12, pages 1023--1031. ACM, 2012. Google ScholarDigital Library
- A. Machanavajjhala, D. Kifer, J. Gehrke, and M. Venkitasubramaniam. L-diversity: Privacy beyond k-anonymity. ACM Trans. Knowl. Discov. Data, 1(1), 2007. Google ScholarDigital Library
- A. Mislove, B. Viswanath, K. P. Gummadi, and P. Druschel. You are who you know: inferring user profiles in online social networks. In Proceedings of the third ACM international conference on Web search and data mining, WSDM '10, pages 251--260. ACM, 2010. Google ScholarDigital Library
- A. Narayanan and V Shmatikov. De-anonymizing social networks. In Proceedings of the 2009 30th IEEE Symposium on Security and Privacy, SP'09, pages 173--187. IEEE Computer Society, 2009. Google ScholarDigital Library
- D. Rao, D. Yarowsky, A. Shreevats, and M. Gupta. Classifying latent user attributes in twitter. In Proceedings of the 2nd international workshop on Search and mining user-generated contents, SMUC '10, pages 37--44. ACM, 2010. Google ScholarDigital Library
- A. Sadilek, H. Kautz, and J. P. Bigham. Finding your friends and following them to where you are. In Proceedings of the fifth ACM international conference on Web search and data mining, WSDM'12, pages 723--732. ACM, 2012. Google ScholarDigital Library
- E. Stamatatos. A survey of modern authorship attribution methods. J. Am. Soc. Inf Sci. Technol., 60(3):538--556, mar 2009. Google ScholarDigital Library
- L. Sweeney. k-anonymity: a model for protecting privacy. Int. J. Uncertain. Fuzziness Knowl-Based Syst., 10(5):557--570, 2002. Google ScholarDigital Library
- T. Wang, M. Srivatsa, D. Agrawal, and L. Liu. Modeling data flow in socio-information networks: a risk estimation approach. In Proceedings of the 16th ACM symposium on Access control models and technologies, SACMAT '11, pages 113--122. ACM, 2011. Google ScholarDigital Library
- E. Zheleva and L. Getoor. Privacy in social networks: a survey. In C. Aggarwal, editor, Social Network Data Analytics, chapter 9, pages 247--276. Springer, March 2011.Google Scholar
Index Terms
- Account Reachability: A Measure of Privacy Risk for Exposure of a User's Multiple SNS Accounts
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