Utility-preserving differentially private data releases via individual ranking microaggregation
نویسندگان
چکیده
منابع مشابه
Utility-Preserving Differentially Private Data Releases Via Individual Ranking Microaggregation
Being able to release and exploit open data gathered in information systems is crucial for researchers, enterprises and the overall society. Yet, these data must be anonymized before release to protect the privacy of the subjects to whom the records relate. Differential privacy is a privacy model for anonymization that offers more robust privacy guarantees than previous models, such as k-anonym...
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Query logs are of great interest for scientists and companies for research, statistical and commercial purposes. However, the availability of query logs for secondary uses raises privacy issues since they allow the identification and/or revelation of sensitive information about individual users. Hence, query anonymization is crucial to avoid identity disclosure. To enable the publication of pri...
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In addition to the general-purpose SSE-based utility evaluation conducted and discussed in the body of the article, in this appendix we provide evaluation results for a specific data use, namely counting queries. The reason of focusing on this data use is that many related works on differentially-private data publishing aim at preserving the utility for counting queries over protected data [12–...
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Differential privacy has recently emerged in private statistical data release as one of the strongest privacy guarantees. Releasing synthetic data that mimic original data with Differential privacy provides a promising way for privacy preserving data sharing and analytics while providing a rigorous privacy guarantee. However, to this date there is no open-source tools that allow users to genera...
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Microaggregation by individual ranking is one of the most commonly applied disclosure control techniques for continuous microdata. The paper studies the effect of microaggregation by individual ranking on the least squares estimation of a multiple linear regression model in continuous variables. It is shown that the naive parameter estimates are asymptotically unbiased. Moreover, the naive leas...
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ژورنال
عنوان ژورنال: Information Fusion
سال: 2016
ISSN: 1566-2535
DOI: 10.1016/j.inffus.2015.11.002