Data privacy through optimal k-anonymization
WebThis paper proposes and evaluates an optimization algorithm for the powerful de-identification procedure known as k-anonymization. A k-anonymized dataset has the property that each record is indistinguishable from at least k - 1 others. Even simple restrictions of optimized k-anonymity are NP-hard, leading to significant computational … WebTo use k-anonymity to process a dataset so that it can be released with privacy protection, a data scientist must first examine the dataset and decide if each attribute (column) is an identifier(identifying), a non-identifier(not-identifying), or a …
Data privacy through optimal k-anonymization
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WebData Anonymization: K-anonymity Sensitivity Analysis ... Sweeney and Samarati define right balance between personal data privacy and data value for k-anonymity as follows [3] [4]: “Let T(A1,...,An) be a table research. ... the through the anonymization process, the racial minorities are suppressed records increase. A huge loss of data ... WebEnter the email address you signed up with and we'll email you a reset link.
WebThis paper proposes and evaluates an optimization algorithm for the powerful de-identification procedure known as k-anonymization. A k-anonymized dataset has the property that each record is indistinguishable from at least k – 1 others. Even simple restrictions of optimized k-anonymity are NP-hard, leading to significant computational … http://www.infocomm-journal.com/wlw/EN/10.11959/j.issn.2096-3750.2024.00066
WebDe-identifying data through common formulations of -anonymity is unfortunately NP-hard if one wishes to guarantee an optimal anonymization [8]. Algorithms that are suitable for … WebThis alert has been successfully added and will be sent to: You will be notified whenever a record that you have chosen has been cited.
WebApr 8, 2005 · Data de-identification reconciles the demand for release of data for research purposes and the demand for privacy from individuals. This paper proposes and …
WebMethods for k-anonymization. To use k-anonymity to process a dataset so that it can be released with privacy protection, a data scientist must first examine the dataset and … innovation ecosystem in educationWebEnter the email address you signed up with and we'll email you a reset link. innovation dictionary definitionWebDe-identifying data through common formulations of -anonymity is unfortunately NP-hard if one wishes to guarantee an optimal anonymization [8]. Algorithms that are suitable for … modern contemporary ranch homesinnovation designer abl educationWebJan 12, 2011 · The k -anonymity model proposed by Samarati and Sweeney is a practical approach for data privacy preservation and has been studied extensively for the last few years. Anonymization methods via generalization or suppression are able to protect private information, but lose valued information. modern contemporary sectional sofasWebOct 22, 2014 · Through experiments on real census data, we show the resulting algorithm can find optimal k-anonymizations under two representative cost measures and a wide … modern contemporary platform bedsWebMay 5, 2005 · This paper proposes and evaluates an optimization algorithm for the powerful de-identification procedure known as k-anonymization. A k-anonymized dataset has the property that each record is ... innovation et invention technologie