Efficient systematic clustering method for k-anonymization
Article
Article Title | Efficient systematic clustering method for k-anonymization |
---|---|
ERA Journal ID | 19224 |
Article Category | Article |
Authors | Kabir, Md Enamul (Author), Wang, Hua (Author) and Bertino, Elisa (Author) |
Journal Title | Acta Informatica |
Journal Citation | 48 (1), pp. 51-66 |
Number of Pages | 16 |
Year | 2011 |
Place of Publication | Germany |
ISSN | 0001-5903 |
1432-0525 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s00236-010-0131-6 |
Web Address (URL) | https://link.springer.com/article/10.1007/s00236-010-0131-6 |
Abstract | This paper presents a clustering (Clustering partitions record into clusters such that records within a cluster are similar to each other, while records in different clusters are most distinct from one another.) based k-anonymization technique to minimize the information loss while at the same time assuring data quality. Privacy preservation of individuals has drawn considerable interests in data mining research. The k-anonymity model proposed by Samarati and Sweeney is a practical approach for data privacy preservation and has been studied extensively for the last few years. Anonymization methods via generalization or suppression are able to protect private information, but lose valued information. The challenge is how to minimize the information loss during the anonymization process. We refer to the challenge as a systematic clustering problem for k-anonymization which is analysed in this paper. The proposed technique adopts group-similar data together and then anonymizes each group individually. The structure of systematic clustering problem is defined and investigated |
Keywords | k-anonymity; systematic clustering; privacy |
ANZSRC Field of Research 2020 | 460903. Information modelling, management and ontologies |
350715. Quality management | |
460499. Cybersecurity and privacy not elsewhere classified | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Department of Mathematics and Computing |
Purdue University, United States | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q0750/efficient-systematic-clustering-method-for-k-anonymization
1914
total views21
total downloads1
views this month0
downloads this month