K-anonymity on sensitive transaction items

Shyue Liang Wang, Yu Chuan Tsai, Hung-Yu Kao, Tzung Pei Hong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

K-anonymity-based techniques [9], [11], [15]-[17] have been the main anonymization techniques on relational data ad transactional data to protect privacy against re-identification attacks. Assuming the existence of both sensitive attributes and quasi-identifier (QI) attributes, a relational dataset D is k-anonymous if every record in D has at least k-1 other records with identical quasi-identifier attribute values, but with different sensitive attribute values. However, existing k-anonymity on transactional data treats all items as quasi-identifiers. The anonymized data set has k identical transactions in groups and suffered from lower data utility [6]-[7][10][18]-[19] . In this work, we propose a new anonymity concept on transactional data with quasi-identifier items and sensitive items (SI). For a transaction that contains sensitive items, there must exist at least k-1 other identical transactions [5][20]. For a transaction that does not contain sensitive item, no anonymization is required. A transactional data set satisfying this property is called sensitive k-anonymous. We proposed two algorithms, Sensitive Transaction Neighbors (STN) and Gray Sort Clustering (GSC), by adding/deleting QI items and adding SI items to achieve sensitive k-anonymity on transactional data. Extensive numerical experiments were given to demonstrate the characteristics of the proposed concept and approaches.

Original languageEnglish
Title of host publicationProceedings - 2011 IEEE International Conference on Granular Computing, GrC 2011
Pages723-727
Number of pages5
DOIs
Publication statusPublished - 2011 Dec 1
Event2011 IEEE International Conference on Granular Computing, GrC 2011 - Kaohsiung, Taiwan
Duration: 2011 Nov 82011 Nov 10

Publication series

NameProceedings - 2011 IEEE International Conference on Granular Computing, GrC 2011

Other

Other2011 IEEE International Conference on Granular Computing, GrC 2011
CountryTaiwan
CityKaohsiung
Period11-11-0811-11-10

All Science Journal Classification (ASJC) codes

  • Software

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