Efficient multivariate data-oriented microaggregation
The VLDB Journal — The International Journal on Very Large Data Bases
A polynomial-time approximation to optimal multivariate microaggregation
Computers & Mathematics with Applications
Micro-aggregation-based heuristics for p-sensitive k-anonymity: one step beyond
PAIS '08 Proceedings of the 2008 international workshop on Privacy and anonymity in information society
A Linear-Time Multivariate Micro-aggregation for Privacy Protection in Uniform Very Large Data Sets
MDAI '08 Sabadell Proceedings of the 5th International Conference on Modeling Decisions for Artificial Intelligence
International Journal of Data Analysis Techniques and Strategies
Reconciling semantic conflicts in electronic patient data exchange
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
Microdata protection through approximate microaggregation
ACSC '09 Proceedings of the Thirty-Second Australasian Conference on Computer Science - Volume 91
An approximate microaggregation approach for microdata protection
Expert Systems with Applications: An International Journal
Forecasting using rules extracted from privacy preservation neural network
MIWAI'11 Proceedings of the 5th international conference on Multi-Disciplinary Trends in Artificial Intelligence
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Regional healthcare initiatives seek to improve the quality of healthcare by collecting, analyzing, and disseminating information about chronic diseases such as diabetes. The datarequired to support such initiatives comes from several organizations such as insurers, physicians, hospitals, pharmacies and labs each of which gather and maintain data for thepurpose of healthcare delivery. Accessing data in this distributed and heterogeneous environment is difficult and has to deal with well-documented issues such as resolving semantic conflicts, multiple query languages etc. Data warehousing and mediator-basedarchitectures are often proposed and used in these settings. In this paper, we focus on mediator-based architectures and the privacy problems that arise in the healthcare context owing to the linkage of information about patients, physicians, and diseases enabled by the mediator. Current proposals for security-conscious mediators do not address inferential disclosure resulting from record linkage. In particular, we study the problem of interval inference, a specific kind of disclosure that arises when participants are able to compute tight bounds on sensitive values of other participants, based on the aggregate information published by the mediator. We illustrate our approach with a real world example and propose an "audit and aggregate" methodology that chooses the optimal level of aggregation of the data taking into account both the risk of disclosure as well as the utility of the released data to legitimate users.