NDLI: On the privacy preserving properties of random data perturbation techniques

Content Provider	IEEE Xplore Digital Library
Author	Kargupta, H. Datta, S. Wang, Q. Sivakumar, Krishnamoorthy
Copyright Year	2003
Description	Author affiliation: Dept. of Comput. Sci. & Electr. Eng., Univ. of Maryland Baltimore County, MD, USA (Kargupta, H.; Datta, S.)
Abstract	Privacy is becoming an increasingly important issue in many data mining applications. This has triggered the development of many privacy-preserving data mining techniques. A large fraction of them use randomized data distortion techniques to mask the data for preserving the privacy of sensitive data. This methodology attempts to hide the sensitive data by randomly modifying the data values often using additive noise. We question the utility of the random value distortion technique in privacy preservation. We note that random objects (particularly random matrices) have "predictable" structures in the spectral domain and it develops a random matrix-based spectral filtering technique to retrieve original data from the dataset distorted by adding random values. We present the theoretical foundation of this filtering method and extensive experimental results to demonstrate that in many cases random data distortion preserve very little data privacy. We also point out possible avenues for the development of new privacy-preserving data mining techniques like exploiting multiplicative and colored noise for preserving privacy in data mining applications.
Sponsorship	IEEE Comput. Soc. Tech. Committee on Computational Intelligence IEEE Comput. Soc. Tech. Committee on Pattern Analysis and Machine Intelligence
Starting Page	99
Ending Page	106
File Size	450080
Page Count	8
File Format	PDF
ISBN	0769519784
DOI	10.1109/ICDM.2003.1250908
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-11-22
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data privacy Perturbation methods Data mining Filtering Computer science Application software Additive noise Information retrieval Colored noise Telecommunication traffic
Content Type	Text
Resource Type	Article

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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
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