Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
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
L-diversity: Privacy beyond k-anonymity
ACM Transactions on Knowledge Discovery from Data (TKDD)
ARES '11 Proceedings of the 2011 Sixth International Conference on Availability, Reliability and Security
Automatic anonymous fingerprinting of text posted on social networking services
IWDW'12 Proceedings of the 11th international conference on Digital Forensics and Watermaking
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The anonymization of sensitive microdata (e.g. medical health records) is a widely-studied topic in the research community. A still unsolved problem is the limited informative value of anonymized microdata that often rules out further processing (e.g. statistical analysis). Thus, a tradeoff between anonymity and data precision has to be made, resulting in the release of partially anonymized microdata sets that still can contain sensitive information and have to be protected against unrestricted disclosure. Anonymization is often driven by the concept of k-anonymity that allows fine-grained control of the anonymization level. In this paper, we present an algorithm for creating unique fingerprints of microdata sets that were partially anonymized with k-anonymity techniques. We show that it is possible to create different versions of partially anonymized microdata sets that share very similar levels of anonymity and data precision, but still can be uniquely identified by a robust fingerprint that is based on the anonymization process.