Data networks
Security and inference in multilevel database and knowledge-base systems
SIGMOD '87 Proceedings of the 1987 ACM SIGMOD international conference on Management of data
Anonymous Web transactions with Crowds
Communications of the ACM
ContactMap: using personal social networks to organize communication in a social desktop
CSCW '02 Proceedings of the 2002 ACM on Computer supported cooperative work video program
Privacy critics: UI components to safeguard users' privacy
CHI '99 Extended Abstracts on Human Factors in Computing Systems
A Privacy Awareness System for Ubiquitous Computing Environments
UbiComp '02 Proceedings of the 4th international conference on Ubiquitous Computing
Privacy through pseudonymity in user-adaptive systems
ACM Transactions on Internet Technology (TOIT)
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Dynamic privacy management: a plug-in service for the middleware in pervasive computing
Proceedings of the 7th international conference on Human computer interaction with mobile devices & services
Privacy constraint processing in a privacy-enhanced database management system
Data & Knowledge Engineering
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Probability and Random Processes For EE's (3rd Edition)
Probability and Random Processes For EE's (3rd Edition)
Seven privacy worries in ubiquitous social computing
Proceedings of the 3rd symposium on Usable privacy and security
Measuring Topological Anonymity in Social Networks
GRC '07 Proceedings of the 2007 IEEE International Conference on Granular Computing
Identity Inference as a Privacy Risk in Computer-Mediated Communication
HICSS '09 Proceedings of the 42nd Hawaii International Conference on System Sciences
De-anonymizing Social Networks
SP '09 Proceedings of the 2009 30th IEEE Symposium on Security and Privacy
Social Inference Risk Modeling in Mobile and Social Applications
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 03
Preventing Unwanted Social Inferences with Classification Tree Analysis
ICTAI '09 Proceedings of the 2009 21st IEEE International Conference on Tools with Artificial Intelligence
Towards an information theoretic metric for anonymity
PET'02 Proceedings of the 2nd international conference on Privacy enhancing technologies
PET'02 Proceedings of the 2nd international conference on Privacy enhancing technologies
PinKDD'07 Proceedings of the 1st ACM SIGKDD international conference on Privacy, security, and trust in KDD
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In any situation where a set of personal attributes are revealed, there is a chance that revealed data can be linked back to its owner. Examples of such situations are publishing user profile micro-data or information about social ties, sharing profile information on social networking sites, or revealing personal information in computer-mediated communication (CMC). Measuring user anonymity is the first step to ensuring that the identity of the owner of revealed information cannot be inferred. Most current measures of anonymity ignore important factors such as the probabilistic nature of identity inference, the inferrer's outside knowledge, and the correlation between user attributes. Furthermore, in the social computing domain, variations in personal information and various levels of information exchange among users make the problem more complicated. We present an information-entropy-based realistic estimation of the user anonymity level to deal with these issues in social computing in an effort to help predict the identity inference risks. We then address implementation issues of online protection by proposing complexity reduction methods that take advantage of basic information entropy properties. Our analysis and delay estimation based on experimental data show that our methods are viable, effective, and efficient in facilitating privacy in social computing and synchronous CMCs.