CIPHERMATCH: Accelerating Homomorphic Encryption-Based String Matching via Memory-Efficient Data Packing and In-Flash Processing

2025-03-12

AI Summary

Homomorphic encryption (HE)-based string matching suffers from high latency and data movement bottlenecks due to ciphertext expansion and expensive homomorphic operations—particularly multiplication and rotation. To address this, we propose a hardware–software co-design: first, a memory-efficient ciphertext packing scheme that eliminates redundant homomorphic operations; second, an in-flash processing (IFP) architecture tailored for HE string matching to enable near-data computing. Compared to state-of-the-art pure-software approaches, our software-only optimization achieves 42.9× speedup and 17.6× energy reduction; integrating IFP further delivers end-to-end acceleration of 136.9× and 256.4× energy savings. This work marks the first application of IFP to HE string matching, establishing a practical and efficient hardware acceleration paradigm for privacy-sensitive applications—including encrypted DNA sequence alignment and secure database search.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Security and Privacy: Privacy-enhancing technologies
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Homomorphic encryption (HE) allows secure computation on encrypted data without revealing the original data, providing significant benefits for privacy-sensitive applications. Many cloud computing applications (e.g., DNA read mapping, biometric matching, web search) use exact string matching as a key operation. However, prior string matching algorithms that use homomorphic encryption are limited by high computational latency caused by the use of complex operations and data movement bottlenecks due to the large encrypted data size. In this work, we provide an efficient algorithm-hardware codesign to accelerate HE-based secure exact string matching. We propose CIPHERMATCH, which (i) reduces the increase in memory footprint after encryption using an optimized software-based data packing scheme, (ii) eliminates the use of costly homomorphic operations (e.g., multiplication and rotation), and (iii) reduces data movement by designing a new in-flash processing (IFP) architecture. We demonstrate the benefits of CIPHERMATCH using two case studies: (1) Exact DNA string matching and (2) encrypted database search. Our pure software-based CIPHERMATCH implementation that uses our memory-efficient data packing scheme improves performance and reduces energy consumption by 42.9X and 17.6X, respectively, compared to the state-of-the-art software baseline. Integrating CIPHERMATCH with IFP improves performance and reduces energy consumption by 136.9X and 256.4X, respectively, compared to the software-based CIPHERMATCH implementation.

Problem

• Accelerates homomorphic encryption-based string matching
• Reduces memory footprint and costly homomorphic operations
• Minimizes data movement with in-flash processing architecture

Innovation

• Optimized data packing reduces memory footprint.
• Eliminates costly homomorphic operations for efficiency.
• In-flash processing architecture minimizes data movement.