An efficient hash-based algorithm for minimal k-anonymity
Paper
Paper/Presentation Title | An efficient hash-based algorithm for minimal k-anonymity |
---|---|
Presentation Type | Paper |
Authors | Sun, Xiaoxun (Author), Li, Min (Author), Wang, Hua (Author) and Plank, Ashley (Author) |
Editors | Dobbie, Gillian and Mans, Bernard |
Journal or Proceedings Title | Conferences in Research and Practice in Information Technology (CRPIT) |
ERA Conference ID | 42479 |
Journal Citation | 74, pp. 101-107 |
Number of Pages | 7 |
Year | 2008 |
Place of Publication | Sydney, Australia |
ISBN | 9781920682552 |
Web Address (URL) of Paper | http://crpit.com/confpapers/CRPITV74Sun.pdf |
Conference/Event | ACSC 2008: 31st Australasian Computer Science Conference |
Australasian Computer Science Conference | |
Event Details | Australasian Computer Science Conference ACSC Rank B B |
Event Details | ACSC 2008: 31st Australasian Computer Science Conference Event Date 22 to end of 25 Jan 2008 Event Location Wollongong, Australia |
Abstract | A number of organizations publish microdata for purposes such as public health and demographic research. Although attributes of microdata that clearly identify individuals, such as name and medical care card number, are generally removed, these databases can sometimes be joined with other public databases on attributes such as Zip code, Gender and Age to re- identify individuals who were supposed to remain k-anonymity is a technique that prevents 'linking' attacks by generalizing and/or suppressing portions of the released microdata so that no individual can be uniquely distinguished from a group of size k. |
Keywords | microdata; hash-based algorithm; k-anonymity |
ANZSRC Field of Research 2020 | 460599. Data management and data science not elsewhere classified |
460499. Cybersecurity and privacy not elsewhere classified | |
469999. Other information and computing sciences not elsewhere classified | |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | Department of Mathematics and Computing |
https://research.usq.edu.au/item/9yv54/an-efficient-hash-based-algorithm-for-minimal-k-anonymity
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