Name
Abstract | ||
|---|---|---|
Deep learning as a service (DLaaS) has been intensively studied to facilitate the wider deployment of the emerging deep learning applications. However, DLaaS may compromise the privacy of both clients and cloud servers. Although some privacy preserving deep neural network (DNN) techniques have been proposed by composing cryptographic primitives, the challenges on computational efficiency have not been fully addressed due to the complexity of DNN models and expensive cryptographic primitives. In this paper, we propose a novel privacy preserving cloud-based DNN inference framework ("PROUD"), which greatly improves the computational efficiency. Finally, we conduct experiments on two datasets to validate the effectiveness and efficiency for the PROUD while benchmarking with the state-of-the-art techniques. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICASSP39728.2021.9413820 | 2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shangyu Xie | 1 | 7 | 4.79 |
| Bingyu Liu | 2 | 2 | 4.08 |
| Yuan Hong | 3 | 184 | 18.71 |