Secure Overcomplete Dictionary Learning for Sparse Representation

Takayuki NAKACHI  Yukihiro BANDOH  Hitoshi KIYA  

IEICE TRANSACTIONS on Information and Systems   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 —)
sparse representation,  dictionary learning,  random unitary transform,  secure computation,  

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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.