A new technique ensuring privacy in big data: K -anonymity without prior value of the threshold k
نویسندگان
چکیده
منابع مشابه
($k$,$\epsilon$)-Anonymity: $k$-Anonymity with $\epsilon$-Differential Privacy
The explosion in volume and variety of data offers enormous potential for research and commercial use. Increased availability of personal data is of particular interest in enabling highly customised services tuned to individual needs. Preserving the privacy of individuals against reidentification attacks in this fast-moving ecosystem poses significant challenges for a one-size fits all approach...
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Microdata is the basis of statistical studies. If microdata is released, it can leak sensitive information about the participants, even if identifiers like name or social security number are removed. A proper anonymization for statistical microdata is essential. K-anonymity has been intensively discussed as a measure for anonymity in statistical data. Quasi identifiers are attributes that might...
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Publishing data about individuals without revealing sensitive information about them is an important problem. In recent years, a new definition of privacy called k-anonymity has gained popularity. In a k-anonymized dataset, each record is indistinguishable from at least k−1 other records with respect to certain “identifying” attributes. In this paper we show with two simple attacks that a k-ano...
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Privacy preservation is an important issue in the release of data for mining purposes. The k-anonymity model has been introduced for protecting individual identification. Recent studies show that a more sophisticated model is necessary to protect the association of individuals to sensitive information. In this paper, we propose an (α, k)-anonymity model to protect both identifications and relat...
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Objective: There is increasing pressure to share health information and even make it publicly availab However, such disclosures of personal health information raise serious privacy concerns. To alleviate such concerns, it is possible to anonymize the data before disclosure. One popular anonymization approach is kanonymity. There have been no evaluations of the actual re-identification probabili...
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ژورنال
عنوان ژورنال: Procedia Computer Science
سال: 2018
ISSN: 1877-0509
DOI: 10.1016/j.procs.2018.01.097