Providing group anonymity using wavelet transform

Authors:
Oleg Chertov;Dan Tavrov
Affiliations:
Faculty of Applied Mathematics, National Technical University of Ukraine,"Kyiv Polytechnic Institute", Kyiv, Ukraine;Faculty of Applied Mathematics, National Technical University of Ukraine,"Kyiv Polytechnic Institute", Kyiv, Ukraine
Venue:
BNCOD'10 Proceedings of the 27th British national conference on Data Security and Security Data
Year:
2010

Citing 9
Cited 0

Privacy-preserving data mining

SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Practical Data-Oriented Microaggregation for Statistical Disclosure Control

IEEE Transactions on Knowledge and Data Engineering
Randomization in privacy preserving data mining

ACM SIGKDD Explorations Newsletter
A survey on wavelet applications in data mining

ACM SIGKDD Explorations Newsletter
k-anonymity: a model for protecting privacy

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Random-data perturbation techniques and privacy-preserving data mining

Knowledge and Information Systems
Singular value decomposition based data distortion strategy for privacy protection

Knowledge and Information Systems
NNMF-Based Factorization Techniques for High-Accuracy Privacy Protection on Non-negative-valued Datasets

ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Wavelet-Based Data Perturbation for Simultaneous Privacy-Preserving and Statistics-Preserving

ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops

Quantified Score

Hi-index	0.00

Visualization

Abstract

Providing public access to unprotected digital data can pose a threat of unwanted disclosing the restricted information. The problem of protecting such information can be divided into two main subclasses, namely, individual and group data anonymity. By group anonymity we define protecting important data patterns, distributions, and collective features which cannot be determined through analyzing individual records only. An effective and comparatively simple way of solving group anonymity problem is doubtlessly applying wavelet transform. It's easy-to-implement, powerful enough, and might produce acceptable results if used properly. In the paper, we present a novel method of using wavelet transform for providing group anonymity; it is gained through redistributing wavelet approximation values, along with simultaneous fixing data mean value and leaving wavelet details unchanged (or proportionally altering them). Moreover, we provide a comprehensive example to illustrate the method.