Secure Overcomplete Dictionary Learning for Sparse Representation
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E103-D No.1 pp.50-58
- Publication Date
- 2020/01/01
- Publicized
- 2019/10/09
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2019MUP0009
- Type of Manuscript
- Special Section PAPER (Special Section on Enriched Multimedia — Application of Multimedia Technology and Its Security —)
- Category
Authors
Takayuki NAKACHI
NTT Corporation
Yukihiro BANDOH
NTT Corporation
Hitoshi KIYA
Tokyo Metropolitan University
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
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
Copy
@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},}
Copy
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 -
English
