k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
L-diversity: Privacy beyond k-anonymity
ACM Transactions on Knowledge Discovery from Data (TKDD)
Looking at, looking up or keeping up with people?: motives and use of facebook
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A survey of modern authorship attribution methods
Journal of the American Society for Information Science and Technology
ASONAM '09 Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining
De-anonymizing Social Networks
SP '09 Proceedings of the 2009 30th IEEE Symposium on Security and Privacy
You are who you know: inferring user profiles in online social networks
Proceedings of the third ACM international conference on Web search and data mining
Classifying latent user attributes in twitter
SMUC '10 Proceedings of the 2nd international workshop on Search and mining user-generated contents
Proceedings of the first ACM conference on Data and application security and privacy
Modeling Unintended Personal-Information Leakage from Multiple Online Social Networks
IEEE Internet Computing
Modeling data flow in socio-information networks: a risk estimation approach
Proceedings of the 16th ACM symposium on Access control models and technologies
Finding your friends and following them to where you are
Proceedings of the fifth ACM international conference on Web search and data mining
Privacy in Social Networks: How Risky is Your Social Graph?
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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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.