Presentation | 2012-11-07 Differential Privacy of Positive Semi-definite Matrices Jun SAKUMA, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | We consider differential privacy of positive semidefinite matrices. We introduce two new randomization schemes; One is a mechanism which applies low-rank approximation as a post process after regular element-wise randomization with Laplace noises. The other is based on eigendecomposition using the matrix von-Mises Fisher distribution. Given a fixed privacy budget, we prove that our mechanisms achieve relatively better accuracy if lower-rank approximation of the output is acceptable. We experimentally demonstrate that low-rank approximation helps to control the accuracy-privacy trade-off with experiments of collaborative filtering and spectral network analysis. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | differential privacy / low-rank approximation / collaborative filtering / spectral analysis / graph Laplacian |
Paper # | IBISML2012-46 |
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Committee | IBISML |
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Conference Date | 2012/10/31(1days) |
Place (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) | Differential Privacy of Positive Semi-definite Matrices |
Sub Title (in English) | |
Keyword(1) | differential privacy |
Keyword(2) | low-rank approximation |
Keyword(3) | collaborative filtering |
Keyword(4) | spectral analysis |
Keyword(5) | graph Laplacian |
1st Author's Name | Jun SAKUMA |
1st Author's Affiliation | Graduate School oi SIE, University of Tsukuba:Japan Sciance and Technology Agency, PRESTO() |
Date | 2012-11-07 |
Paper # | IBISML2012-46 |
Volume (vol) | vol.112 |
Number (no) | 279 |
Page | pp.pp.- |
#Pages | 8 |
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