NDLI: On Unifying Privacy and Uncertain Data Models

Content Provider	IEEE Xplore Digital Library
Author	Aggarwal, C.C.
Copyright Year	2008
Description	Author affiliation: T.J. Watson Res. Center, IBM, Hawthorne, NY (Aggarwal, C.C.)
Abstract	The problem of privacy-preserving data mining has been studied extensively in recent years because of the increased amount of personal information which is available to corporations and individuals. Most privacy transformations use some form of data perturbation or representational ambiguity in order to reduce the risk of identification. The final results from privacy transformation methods often require the underlying applications to be modified in order to work with the new representation of the data. Since the end results of privacy-transformation methods have not been standardized, the required modifications may vary with the method used for the privacy transformation. In some cases, it can be an enormous effort to re-design applications to work with the anonymized data. While the results of privacy-transformation methods are a natural form of uncertain data, the two problems have generally been studied independently. In this paper, we make a first attempt to unify the two fields, and propose a privacy transformation for which existing uncertain data management tools can be directly used. This is a great advantage, since it means that the wide spectrum of research available for uncertain data management can also be used for privacy-preserving data mining. We propose an uncertain version of the k-anonymity model which is related to the well known deterministic model of k- anonymity. The uncertain version of the k-anonymity model has the additional feature of introducing greater uncertainty for the adversary over an equivalent deterministic model. As specific instantiations of this approach, we test the effectiveness of the privacy transformation on the problems of query estimation and classification, and show that the technique retains greater accuracy than other k-anonymity models.
Starting Page	386
Ending Page	395
File Size	1555404
Page Count	10
File Format	PDF
ISBN	9781424418367
DOI	10.1109/ICDE.2008.4497447
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-04-07
Publisher Place	Mexico
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data privacy Data models Data mining Aggregates Uncertainty Testing Data analysis Design methodology
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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