access icon free Personalised anonymity for microdata release

© The Institution of Engineering and Technology
Received 25/12/2016, Accepted 23/02/2018, Revised 05/02/2018, Published 27/02/2018

Individual privacy protection in the released data sets has become an important issue in recent years. The release of microdata provides a significant information resource for researchers, whereas the release of person-specific data poses a threat to individual privacy. Unfortunately, microdata could be linked with publicly available information to exactly re-identify individuals’ identities. In order to relieve privacy concerns, data has to be protected with a privacy protection mechanism before its disclosure. The k-anonymity model is an important method in privacy protection to reduce the risk of re-identification in microdata release. This model necessitates the indistinguishably of each tuple from at least k − 1 other tuples in the released data. While k-anonymity preserves the truthfulness of the released data, the privacy level of anonymisation is same for each individual. However, different individuals have different privacy needs in the real world. Thereby, personalisation plays an important role in supporting the notion of individual privacy protection. This study proposes a personalised anonymity model that provides distinct privacy levels for each individual by offering them to control their anonymity on the released data. To satisfy the personal anonymity requirements with low information loss, the authors introduce a clustering based algorithm.

Inspec keywords: data privacy; security of data

Other keywords: different individuals; significant information resource; k-anonymity model; microdata release; different privacy needs; personal anonymity requirements; individual privacy protection; personalised anonymity model; publicly available information; privacy concerns; person-specific data; privacy protection mechanism; distinct privacy levels; released data sets

Subjects: Data security

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