Full Citation

Title: Microdata Protection Method Through Microaggregation: A Systematic approach

Citation Type: Journal Article

Publication Year: 2012

ISBN:

ISSN:

DOI:

NSFID:

PMCID:

PMID:

Abstract: Microdata protection in statistical databases has recently become a major societal concern and has been intensively studied in recent years. Statistical Disclosure Control (SDC) is often applied to statistical databases before they are released for public use. Microaggregation for SDC is a family of methods to protect microdata from individual identification. SDC seeks to protect microdata in such a way that can be published and mined without providing any private information that can be linked to specific individuals. Microaggregation works by partitioning the microdata into groups of at least k records and then replacing the records in each group with the centroid of the group. This paper presents a clustering-based microaggregation method to minimize the information loss. The proposed technique adopts to group similar records together in a systematic way and then anonymized with the centroid of each group individually. The structure of systematic clustering problem is defined and investigated and an algorithm of the proposed problem is developed. Experimental results show that our method attains a reasonable dominance with respect to both information loss and execution time than the most popular heuristic algorithm called Maximum Distance to Average Vector (MDAV).

Url: https://eprints.usq.edu.au/22945/1/8734-19182-1-PB_%281%29.pdf

User Submitted?: No

Authors: Kabir, Enamul; Wang, Hua

Periodical (Full): JOURNAL OF SOFTWARE

Issue: 11

Volume: 7

Pages: 2415-2423

Data Collections: IPUMS USA

Topics: Other, Population Data Science

Countries: United States