Date of Award
Fall 12-2021
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Electrical & Computer Engineering
Program/Concentration
Electrical and Computer Engineering
Committee Director
Hongyi Wu
Committee Member
Chunsheng Xin
Committee Member
Jiang Li
Committee Member
Sachin Shetty
Abstract
Deep Learning (DL) has shown unrivalled performance in many applications such as image classification, speech recognition, anomalous detection, and business analytics. While end users and enterprises own enormous data, DL talents and computing power are mostly gathered in technology giants having cloud servers. Thus, data owners, i.e., the clients, are motivated to outsource their data, along with computationally-intensive tasks, to the server in order to leverage the server’s abundant computation resources and DL talents for developing cost-effective DL solutions. However, trust is required between the server and the client to finish the computation tasks (e.g., conducting inference for the newly-input data from the client, based on a well-trained model at the server) otherwise there could be the data breach (e.g., leaking data from the client or the proprietary model parameters from the server). Privacy-preserving DL takes data privacy into account where various data-encryption based techniques are adopted. However, the efficiency of linear and nonlinear computation for each DL layer remains a fundamental challenge in practice due to the intrinsic intractability and complexity of privacy-preserving primitives (e.g., Homomorphic Encryption (HE) and Garbled Circuits (GC)). As such, this dissertation targets deeply optimizing state-of-the-art frameworks as well as newly designing efficient modules by joint linear and nonlinear computation, with data encryption, to further boost the overall performance of privacy-preserving DL. Four contributions are made.
Rights
In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
DOI
10.25777/6hgt-cv97
ISBN
9798762197410
Recommended Citation
Zhang, Qiao.
"Joint Linear and Nonlinear Computation with Data Encryption for Efficient Privacy-Preserving Deep Learning"
(2021). Doctor of Philosophy (PhD), Dissertation, Electrical & Computer Engineering, Old Dominion University, DOI: 10.25777/6hgt-cv97
https://digitalcommons.odu.edu/ece_etds/230