Presentation 2010-06-14
Privacy Preservation in Online Prediction
Jun SAKUMA, Hiromi ARAI,
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Abstract(in English) In this paper, we consider online prediction from expert advice in a situation where each expert observes its own loss at each time while the loss cannot be disclosed to others for reasons of privacy or confidentiality preservation. Our secure exponential weighting scheme enables exploitation of such private loss values by making use of cryptographic tools. We proved that the regret bound of the secure exponential weighting is the same or almost the same with the well-known exponential weighting scheme in the full information model. In addition, we prove theoretically that the secure exponential weighting is privacy-preserving in the sense of secure function evaluation.
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Keyword(in English) online prediction / regret / minimax / privacy / secure multi-party computation
Paper # IBISML2010-9
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Committee IBISML
Conference Date 2010/6/7(1days)
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Registration To Information-Based Induction Sciences and Machine Learning (IBISML)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Privacy Preservation in Online Prediction
Sub Title (in English)
Keyword(1) online prediction
Keyword(2) regret
Keyword(3) minimax
Keyword(4) privacy
Keyword(5) secure multi-party computation
1st Author's Name Jun SAKUMA
1st Author's Affiliation Graduate School of SIE, University of Tsukuba:Japan Scienece and Technology Agency()
2nd Author's Name Hiromi ARAI
2nd Author's Affiliation Graduate School of SIE, University of Tsukuba
Date 2010-06-14
Paper # IBISML2010-9
Volume (vol) vol.110
Number (no) 76
Page pp.pp.-
#Pages 8
Date of Issue