Name
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1587/transinf.2019MUP0009 | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS |
Keywords | Field | DocType |
sparse representation, dictionary learning, random unitary transform, secure computation | Dictionary learning,Pattern recognition,Computer science,Sparse approximation,Speech recognition,Artificial intelligence | Journal |
Volume | Issue | ISSN |
E103D | 1 | 1745-1361 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| takayuki nakachi | 1 | 52 | 16.65 |
| Yukihiro Bandoh | 2 | 2 | 1.85 |
| Hitoshi Kiya | 3 | 616 | 113.80 |