Data privacy protection by kernel subspace projection and generalized eigenvalue decomposition

Konstantinos Diamantaras, Sun Yuan Kung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Various internet services, including cloud providers and social networks collect large amounts of information that needs to be processed for statistical or other reasons without breaching user privacy. We present a novel approach where privacy protection can be viewed as a data transformation problem. The problem is formulated as a pair of classification tasks, (a) a privacy-insensitive and (b) a privacy-sensitive task. Then privacy protection is the requirement that, given the transformed data, no classification algorithm may perform well on the sensitive task while hurting the performance on the insensitive task as little as possible. To that end, we introduce a novel criterion called Multiclass Discriminant Ratio which is optimized using the generalized eigenvalue decomposition of a pair of between class scatter matrices. We then formulate a nonlinear extension of this approach using the kernel GED method. Our proposed methods are evaluated using the Human Activity Recognition data set. Using the kernel projected data the performance of the User recognition task is reduced by 89% while the Activity recognition task is reduced only by 7.8%.

Original languageEnglish (US)
Title of host publication2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
EditorsKostas Diamantaras, Aurelio Uncini, Francesco A. N. Palmieri, Jan Larsen
PublisherIEEE Computer Society
ISBN (Electronic)9781509007462
DOIs
StatePublished - Nov 8 2016
Event26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings - Vietri sul Mare, Salerno, Italy
Duration: Sep 13 2016Sep 16 2016

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume2016-November
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Other

Other26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
CountryItaly
CityVietri sul Mare, Salerno
Period9/13/169/16/16

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Signal Processing

Keywords

  • Kernel Generalized Eigenvalue Decomposition
  • Privacy Protection
  • Subspace methods

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  • Cite this

    Diamantaras, K., & Kung, S. Y. (2016). Data privacy protection by kernel subspace projection and generalized eigenvalue decomposition. In K. Diamantaras, A. Uncini, F. A. N. Palmieri, & J. Larsen (Eds.), 2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings [7738831] (IEEE International Workshop on Machine Learning for Signal Processing, MLSP; Vol. 2016-November). IEEE Computer Society. https://doi.org/10.1109/MLSP.2016.7738831