Data Leakage In Anonymization Methods Towards explainable machine learning
- 提供方法
- 本サイト上にてダウンロード・閲覧可
- 形態
- 価格
- 一般価格(税込):¥1,100 会員価格(税込):¥880
- 文献番号
- 20219069
- 文献・情報種別
- その他の国際会議
- 掲載ページ
- 1-5(Total 5 p)
- 発行年月
- 2021年 9月
- 出版社
- (公社)自動車技術会
- 言語
- 英語
書誌事項
著者(英) | 1) Martin Torstensson, 2) Felix Rosberg, 3) Boris Durán, 4) Cristofer Englund |
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勤務先(英) | 1) RISE Research Institutes of Sweden, 2) Berge Consulting,Lindholmspiren 3A, 3) RISE Research Institutes of Sweden, 4) RISE Research Institutes of Sweden;Halmstad University |
抄録(英) | Anonymization methods are one potential way of alleviating the risks of capturing personal information during data collections. The work presented here is based on one such method that, in turn, is based on generating images through machine learning to replace the original images. The chosen method merges both the original image and the generated one resulting in a risk of information from the original image leaking through to the final result. Here a possible approach to measure how much influence the original image has on the final product is presented . 翻訳 |