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.

Preview	Text (Accepted Version) Sun_Wang_NSS2010_AV.pdf Download (107kB)
Preview	Text (Published Version) Sun_Wang_NSS2010_PV.pdf Download (262kB)
Preview	Text (Documentation) Binder1.pdf Download (394kB)

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.

Statistics for this ePrint Item

Item Type:	Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed:	Yes
Item Status:	Live Archive
Additional Information:	© 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Article number 5636092.
Faculty/School / Institute/Centre:	Historic - Faculty of Sciences - Department of Maths and Computing (Up to 30 Jun 2013)
Faculty/School / Institute/Centre:	Historic - Faculty of Sciences - Department of Maths and Computing (Up to 30 Jun 2013)
Date Deposited:	16 Nov 2010 00:11
Last Modified:	14 Oct 2019 00:54
Uncontrolled Keywords:	identity protection; large survey rating data; data; privacy
Fields of Research (2008):	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
Fields of Research (2020):	46 INFORMATION AND COMPUTING SCIENCES > 4604 Cybersecurity and privacy > 460401 Cryptography 46 INFORMATION AND COMPUTING SCIENCES > 4609 Information systems > 460905 Information systems development methodologies and practice 46 INFORMATION AND COMPUTING SCIENCES > 4604 Cybersecurity and privacy > 460499 Cybersecurity and privacy not elsewhere classified
Socio-Economic Objectives (2008):	E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI:	https://doi.org/10.1109/NSS.2010.11
URI:	http://eprints.usq.edu.au/id/eprint/8480

Actions (login required)

Archive Repository Staff Only