| Presentation | 2019-12-12 A Dimensionality Reduction Method with Random Sampling for Privacy-Preserving Machine Learning Ayana Kawamura, Kenta Iida, Hitoshi Kiya, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | In this paper, we propose a dimensionality reduction method with random sampling for privacy-preserving machine learning.Recently, cloud computing is spreading in many fields. However, the cloud computing has some serious issues for end users, such as unauthorized use and leak of data, and privacy compromise, due to unreliability of providers and some accidents.In addition, due to a huge amount of data, a dimensionality reduction technique is generally carried out in the case of applying image data to machine learning.Because of such a situation, we consider a machine learning scheme considering both privacy-preserving and dimensionality reduction.In this paper, we propose a novel dimensionality reduction technique that is carried out by dividing an image into blocks and sampling the blocks randomly.The proposed scheme allows us to preserve visual information of images and maintain the relative spatial relation between images.In addition, the proposed scheme has a feature that any secret-key management is not required.Some face recognition experiments are carried out by using a support vector machine algorithm as an example of machine learning algorithms to demonstrate the effectiveness of the proposed method. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | dimensionality reduction / machine learning / SVM / privacy-preserving |
| Paper # | SIS2019-26 |
| Date of Issue | 2019-12-05 (SIS) |
| Conference Information | |
| Committee | SIS |
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| Conference Date | 2019/12/12(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Okayama University of Science |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Smart Personal Systems, etc. |
| Chair | Takayuki Nakachi(NTT) |
| Vice Chair | Noriaki Suetake(Yamaguchi Univ.) / Tomoaki Kimura(Kanagawa Inst. of Tech.) |
| Secretary | Noriaki Suetake(Tokyo Metropolitan Univ.) / Tomoaki Kimura(Kindai Univ.) |
| Assistant | Hideaki Misawa(National Inst. of Tech., Ube College) / Yukihiro Bandoh(NTT) |
| Paper Information | |
| Registration To | Technical Committee on Smart Info-Media Systems |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Dimensionality Reduction Method with Random Sampling for Privacy-Preserving Machine Learning |
| Sub Title (in English) | |
| Keyword(1) | dimensionality reduction |
| Keyword(2) | machine learning |
| Keyword(3) | SVM |
| Keyword(4) | privacy-preserving |
| 1st Author's Name | Ayana Kawamura |
| 1st Author's Affiliation | Tokyo Metropolitan University(Tokyo Metro. Univ.) |
| 2nd Author's Name | Kenta Iida |
| 2nd Author's Affiliation | Tokyo Metropolitan University(Tokyo Metro. Univ.) |
| 3rd Author's Name | Hitoshi Kiya |
| 3rd Author's Affiliation | Tokyo Metropolitan University(Tokyo Metro. Univ.) |
| Date | 2019-12-12 |
| Paper # | SIS2019-26 |
| Volume (vol) | vol.119 |
| Number (no) | SIS-335 |
| Page | pp.pp.17-21(SIS), |
| #Pages | 5 |
| Date of Issue | 2019-12-05 (SIS) |