2017年12月22日
Privacy-Preserving Visual Learning Using Doubly Permuted Homomorphic Encryption
Proceedings of the IEEE International Conference on Computer Vision
- ,
- ,
- ,
- 巻
- 2017-
- 号
- 開始ページ
- 2059
- 終了ページ
- 2069
- 記述言語
- 英語
- 掲載種別
- 研究論文(国際会議プロシーディングス)
- DOI
- 10.1109/ICCV.2017.225
- 出版者・発行元
- Institute of Electrical and Electronics Engineers Inc.
We propose a privacy-preserving framework for learning visual classifiers by leveraging distributed private image data. This framework is designed to aggregate multiple classifiers updated locally using private data and to ensure that no private information about the data is exposed during and after its learning procedure. We utilize a homomorphic cryptosystem that can aggregate the local classifiers while they are encrypted and thus kept secret. To overcome the high computational cost of homomorphic encryption of high-dimensional classifiers, we (1) impose sparsity constraints on local classifier updates and (2) propose a novel efficient encryption scheme named doublypermuted homomorphic encryption (DPHE) which is tailored to sparse high-dimensional data. DPHE (i) decomposes sparse data into its constituent non-zero values and their corresponding support indices, (ii) applies homomorphic encryption only to the non-zero values, and (iii) employs double permutations on the support indices to make them secret. Our experimental evaluation on several public datasets shows that the proposed approach achieves comparable performance against state-of-the-art visual recognition methods while preserving privacy and significantly outperforms other privacy-preserving methods.
- リンク情報
- ID情報
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- DOI : 10.1109/ICCV.2017.225
- ISSN : 1550-5499
- DBLP ID : journals/corr/YonetaniBKS17
- SCOPUS ID : 85041926327