Open AccessPersonalised anonymity for microdata release
Author(s) -
Can Ozgu
Publication year - 2018
Publication title -
iet information security
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.308
H-Index - 34
eISSN - 1751-8717
pISSN - 1751-8709
DOI - 10.1049/iet-ifs.2016.0613
Subject(s) - microdata (statistics) , anonymity , computer science , internet privacy , k anonymity , tuple , information loss , information privacy , privacy software , computer security , privacy protection , personally identifiable information , mathematics , artificial intelligence , census , population , demography , discrete mathematics , sociology
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.