Presentation | 2010-06-14 Privacy Preservation in Online Prediction Jun SAKUMA, Hiromi ARAI, |
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PDF Download Page | PDF download Page Link |
Abstract(in Japanese) | (See Japanese page) |
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. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | online prediction / regret / minimax / privacy / secure multi-party computation |
Paper # | IBISML2010-9 |
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Committee | IBISML |
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Conference Date | 2010/6/7(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Information-Based Induction Sciences and Machine Learning (IBISML) |
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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 |