Date of Award

Summer 2025

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Electrical & Computer Engineering

Program/Concentration

Electrical and Computer Engineering

Committee Director

Chunsheng Xin

Committee Member

Hongyi Wu

Committee Member

Jinag Li

Committee Member

Lusi Li

Abstract

Deep learning (DL) has become a powerful tool for solving complex problems, but developing DL models typically requires vast datasets, high computational resources, and expert knowledge—barriers that limit accessibility. Machine Learning as a Service (MLaaS) addresses this challenge by allowing resource-rich providers to deliver pre-trained DL models as services. However, privacy concerns arise: clients hesitate to share sensitive data, while providers protect their proprietary models. To address this, privacy-preserving MLaaS integrates cryptographic techniques into DL computations, as seen in frameworks like Cryptonets, SecureML, GAZELLE, CrypTFlow2, Cheetah, and BOLT. Among them, Homomorphic Encryption (HE) enables computation on encrypted data but remains computationally expensive. This dissertation presents four novel contributions to enhance HE-based deep learning efficiency:

First, Hunter introduces structured pruning using HE-friendly features—internal/external structure and weight diagonal—to reduce HE operations like Perm, Mult, and Add while maintaining accuracy. For instance, on VGG-16 (ImageNet), Hunter cuts Perm to 2% and Mult/Add to 14%. Second, MOSAIC addresses pruning structure inflation by assembling compact, HE-friendly units via intelligent channel transformation, reducing costs by 21.14% and 29.49% on VGG-16 under GAZELLE and CrypTFlow2, respectively. StriaNet designs HE-efficient networks from scratch using StriaBlock, which includes ExPerm-Free Convolution and Cross Kernel modules. It minimizes Perm operations and adapts dimensions to HE characteristics, achieving up to 9.78× speedup on ImageNet and 9.24× on CIFAR-10 over ResNet. Lastly, SecDTD enables efficient Transformer inference via early-stage dynamic token dropping. By integrating Pre-Softmax Scoring (MCN) and Fast Oblivious Median Selection (OMSel), it reduces overhead and achieves up to 16.9× speedup. On BERT-base, it delivers up to 4.47× acceleration on GLUE datasets under BOLT and BumbleBee.

Together, these contributions significantly advance the efficiency and practicality of privacy-preserving deep learning, providing a robust foundation for scalable and efficient privacy-preserving MLaaS frameworks.

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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

10.25777/41a9-j271

ISBN

9798293842315

ORCID

0000-0002-1372-656X

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