Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
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
Transforming data to satisfy privacy constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
Data Privacy through Optimal k-Anonymization
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
On the complexity of optimal K-anonymity
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Incognito: efficient full-domain K-anonymity
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
On k-anonymity and the curse of dimensionality
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Mondrian Multidimensional K-Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Utility-based anonymization using local recoding
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
L-diversity: Privacy beyond k-anonymity
ACM Transactions on Knowledge Discovery from Data (TKDD)
Utility-based anonymization for privacy preservation with less information loss
ACM SIGKDD Explorations Newsletter
Local and global recoding methods for anonymizing set-valued data
The VLDB Journal — The International Journal on Very Large Data Bases
ICDT'05 Proceedings of the 10th international conference on Database Theory
An automated data utility clustering methodology using data constraint rules
Proceedings of the 2012 international workshop on Smart health and wellbeing
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As medical data continues to transition to an electronic format, opportunities arise for researchers to use this microdata to discover patterns and increase knowledge in order to improve patient care. Now more than ever, it is critical to protect the identities of the patients contained in these databases. Even after removing obvious "identifier" attributes, such as social security numbers or first and last names, that clearly identify a specific person, it is possible to join "quasi-identifier" attributes from two or more publicly available databases to identify individuals. K-anonymity is an established approach that has been used to ensure that no one individual can be distinguished within a group of at least k individuals. The majority of the proposed approaches implementing k-anonymity have focused on improving the efficiency of algorithms implementing k-anonymity; less emphasis has been put towards ensuring the "utility" of anonymized data from a researchers' perspective. We propose a data utility measurement, called the research value (RV), which evaluates how well common cutoffs for numerical data or groupings in categorical data are preserved during the anonymization process. The proposed algorithm utilizing the new utility function scales efficiently when the number of attributes is large, while still ensuring that the generalization process is dictated by the data content expert's assessment of the utility of the generalized data.