The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
본 논문에서는 희소 표현을 위한 무작위 단위 변환을 기반으로 한 안전한 사전 학습을 제안합니다. 현재 엣지 클라우드 컴퓨팅은 스파스 코딩(Sparse Coding)을 활용한 서비스를 비롯해 다양한 응용 분야로 확산되고 있다. 이러한 상황은 많은 새로운 개인 정보 보호 문제를 야기합니다. 엣지 클라우드 컴퓨팅은 무단 사용, 데이터 유출, 개인 정보 보호 실패 등 최종 사용자에게 몇 가지 심각한 문제를 야기합니다. 제안된 기법은 암호화된 신호에 대한 계산을 허용하는 실용적인 MOD 및 K-SVD 사전 학습 알고리즘을 제공합니다. 우리는 이론적으로 제안이 MOD 및 K-SVD 알고리즘의 암호화되지 않은 변형과 정확히 동일한 사전 학습 추정 성능을 가지고 있음을 증명합니다. 이미지 패치 모델을 기반으로 보안 이미지 모델링에 적용합니다. 마지막으로 합성 데이터에 대한 성능과 자연 이미지를 위한 안전한 이미지 모델링 애플리케이션을 시연합니다.
Takayuki NAKACHI
NTT Corporation
Yukihiro BANDOH
NTT Corporation
Hitoshi KIYA
Tokyo Metropolitan University
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Takayuki NAKACHI, Yukihiro BANDOH, Hitoshi KIYA, "Secure Overcomplete Dictionary Learning for Sparse Representation" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 1, pp. 50-58, January 2020, doi: 10.1587/transinf.2019MUP0009.
Abstract: In this paper, we propose secure dictionary learning based on a random unitary transform for sparse representation. Currently, edge cloud computing is spreading to many application fields including services that use sparse coding. This situation raises many new privacy concerns. Edge cloud computing poses several serious issues for end users, such as unauthorized use and leak of data, and privacy failures. The proposed scheme provides practical MOD and K-SVD dictionary learning algorithms that allow computation on encrypted signals. We prove, theoretically, that the proposal has exactly the same dictionary learning estimation performance as the non-encrypted variant of MOD and K-SVD algorithms. We apply it to secure image modeling based on an image patch model. Finally, we demonstrate its performance on synthetic data and a secure image modeling application for natural images.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019MUP0009/_p
부
@ARTICLE{e103-d_1_50,
author={Takayuki NAKACHI, Yukihiro BANDOH, Hitoshi KIYA, },
journal={IEICE TRANSACTIONS on Information},
title={Secure Overcomplete Dictionary Learning for Sparse Representation},
year={2020},
volume={E103-D},
number={1},
pages={50-58},
abstract={In this paper, we propose secure dictionary learning based on a random unitary transform for sparse representation. Currently, edge cloud computing is spreading to many application fields including services that use sparse coding. This situation raises many new privacy concerns. Edge cloud computing poses several serious issues for end users, such as unauthorized use and leak of data, and privacy failures. The proposed scheme provides practical MOD and K-SVD dictionary learning algorithms that allow computation on encrypted signals. We prove, theoretically, that the proposal has exactly the same dictionary learning estimation performance as the non-encrypted variant of MOD and K-SVD algorithms. We apply it to secure image modeling based on an image patch model. Finally, we demonstrate its performance on synthetic data and a secure image modeling application for natural images.},
keywords={},
doi={10.1587/transinf.2019MUP0009},
ISSN={1745-1361},
month={January},}
부
TY - JOUR
TI - Secure Overcomplete Dictionary Learning for Sparse Representation
T2 - IEICE TRANSACTIONS on Information
SP - 50
EP - 58
AU - Takayuki NAKACHI
AU - Yukihiro BANDOH
AU - Hitoshi KIYA
PY - 2020
DO - 10.1587/transinf.2019MUP0009
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2020
AB - In this paper, we propose secure dictionary learning based on a random unitary transform for sparse representation. Currently, edge cloud computing is spreading to many application fields including services that use sparse coding. This situation raises many new privacy concerns. Edge cloud computing poses several serious issues for end users, such as unauthorized use and leak of data, and privacy failures. The proposed scheme provides practical MOD and K-SVD dictionary learning algorithms that allow computation on encrypted signals. We prove, theoretically, that the proposal has exactly the same dictionary learning estimation performance as the non-encrypted variant of MOD and K-SVD algorithms. We apply it to secure image modeling based on an image patch model. Finally, we demonstrate its performance on synthetic data and a secure image modeling application for natural images.
ER -