k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
ACM SIGKDD Explorations Newsletter
Smooth sensitivity and sampling in private data analysis
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Sanitization models and their limitations
NSPW '06 Proceedings of the 2006 workshop on New security paradigms
Privacy-preserving link discovery
Proceedings of the 2008 ACM symposium on Applied computing
New Efficient Attacks on Statistical Disclosure Control Mechanisms
CRYPTO 2008 Proceedings of the 28th Annual conference on Cryptology: Advances in Cryptology
ARUBA: A Risk-Utility-Based Algorithm for Data Disclosure
SDM '08 Proceedings of the 5th VLDB workshop on Secure Data Management
Efficient Privacy-Preserving Link Discovery
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Geocode Matching and Privacy Preservation
Privacy, Security, and Trust in KDD
Social network classification incorporating link typevalues
ISI'09 Proceedings of the 2009 IEEE international conference on Intelligence and security informatics
Beyond k-Anonymity: A Decision Theoretic Framework for Assessing Privacy Risk
Transactions on Data Privacy
Protecting Privacy Against Record Linkage Disclosure: A Bounded Swapping Approach for Numeric Data
Information Systems Research
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While computer scientists are uniquely situated to incorporate privacy protections in the link analysis algorithms they construct, most computer scientists are unaware of this opportunity and of ways to think about achieving needed protections. The work presented in this writing introduces a new way for computer scientists to think about providing privacy protection within link analysis and introduces the notion of "privacy-enhanced linking" as algorithms that perform link analysis with guarantees of privacy protection modeled after the Fair Information Practices. In this approach, privacy protection is realized by assessing the validity and interpretation of link analysis results such that inappropriate harm to individuals is provably minimized.