Towards identify anonymization in large survey rating data

Sun, Xiaoxun and Wang, Hua (2010) Towards identify anonymization in large survey rating data. In: NSS 2010: 4th International Conference on Network and System Security , 1-3 Sep 2010, Melbourne, Australia.

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Abstract

We study the challenge of identity protection in the large public survey rating data. Even though the survey participants do not reveal any of their ratings, their survey records are potentially identifiable by using information from other public sources. None of the existing anonymisation principles (e.g., k-anonymity, l-diversity, etc.) can effectively prevent such breaches in large survey rating data sets. In this paper, we tackle the problem by defining the (k, epsilon)-anonymity principle. The principle requires for each transaction t in the given survey rating data T, at least (k - 1) other transactions in T must have ratings similar with t, where the similarity is controlled by epsilon. We propose a greedy approach to anonymize survey rating data and apply the method to two real-life data sets to demonstrate their efficiency and practical utility.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Article number 5636092
Depositing User: Dr Hua Wang
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 16 Nov 2010 00:11
Last Modified: 10 Sep 2014 23:17
Uncontrolled Keywords: identity protection; large survey rating data; data; privacy
Fields of Research (FOR2008): 08 Information and Computing Sciences > 0804 Data Format > 080402 Data Encryption
08 Information and Computing Sciences > 0806 Information Systems > 080608 Information Systems Development Methodologies
08 Information and Computing Sciences > 0803 Computer Software > 080303 Computer System Security
Socio-Economic Objective (SEO2008): E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: doi: 10.1109/NSS.2010.11
URI: http://eprints.usq.edu.au/id/eprint/8480

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