PhD Dissertation Defense - Weizhao Jin

Title: Efficiency in Privacy-Preserving Computation via Domain Knowledge  
 
Date and Time: Thursday, January 16th, 2025, from 11:00 AM to 12:00 PM  
 
Location: THH 221
 
Committee: Srivatsan Ravi (CS), Bhaskar Krishnamachari (ECE), Harsha V. Madhyastha (CS), Fred Morstatter (CS)  
 
Abstract: In recent years, the growing reliance on user data for building server-side applications and services has significantly heightened the importance of data privacy. To meet expanding privacy regulations like GDPR, service providers have turned to privacy-preserving methods that maintain computational functionality while protecting user privacy. However, integrating techniques such as homomorphic encryption into application protocols presents a critical challenge: achieving a balance between privacy and efficiency. This thesis explores two distinct domains within privacy-preserving computation, offering practical, domain-specific solutions to address challenges related to overheads and protocol complexity. The focus is on achieving efficient privacy in both machine learning and networks/IoT. To illustrate how leveraging domain-specific insights—from federated learning, entity resolution, and computer networking—can substantially enhance the efficiency of privacy-preserving computation, we first introduce a selective encryption strategy for large-scale federated learning models, reducing overhead by encrypting only sensitive parameters while still maintaining robust privacy guarantees; secondly, we demonstrate how homomorphic encryption can be optimized for deep entity resolution via a two-stage computation scheme and novel techniques including synthetic ranging and polynomial degree optimization that preserve accuracy under encrypted computation; finally, we apply Non-Interactive Zero-Knowledge proofs to achieve lightweight privacy-preserving path validation across multi-authority network slices, ensuring data forwarding compliance without revealing sensitive topology details by utilizing a backward pairwise validation procedure. Taken together, these studies highlight how targeting domain-specific challenges via domain-specific knowledge can yield practical, scalable frameworks for efficient privacy-preserving computation.
 
Zoom Link: https://usc.zoom.us/j/99543392059?pwd=FlQxFqagihzPzEV4tfBaemgHBwOwUM.1  

Location: Mark Taper Hall Of Humanities (THH) - 221

Audiences: Everyone Is Invited

Contact: Weizhao Jin