Full Citation
Title: Quantifying the Effects of Anonymization Techniques Over Micro-Databases
Citation Type: Journal Article
Publication Year: 2022
ISBN:
ISSN:
DOI: 10.1109/TETC.2022.3141754
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Abstract: Micro-databases are unique datasets that contain person-specific information about individuals. Preserving the privacy of such datasets has become a cause for serious concern since this massive repository of personalized data regularly gets published in the public domain. Sanitization mechanisms are specialized techniques that provide the required privacy guarantees to the published data. The work in this article establishes an efficient framework for quantitatively estimating the effectiveness of any privacy-preservation scheme which employs the anonymization principle. In our study, we have introduced an information-theoretic metric termed as Sanitization Degree (η) which assigns a cumulative score in the range [0,1] for a generic anonymization process. The design of our proposed metric is based on the fundamental fact that any sanitization mechanism attempts to reduce the amount of correlated information within the database attributes while simultaneously preserving the utility of the original dataset.
Url: https://ieeexplore.ieee.org/abstract/document/9684233
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Authors: Sadhya, Debanjan; Chakraborty, Bodhi
Periodical (Full): IEEE Transactions on Emerging Topics in Computing
Issue: 4
Volume: 10
Pages: 1979-1992
Data Collections: IPUMS CPS
Topics: Methodology and Data Collection
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