Zk-SNARK for String Match

TL;DR

This paper introduces a privacy-preserving string matching system using zk-SNARKs, combining efficient algorithms and zero-knowledge proofs to verify string presence on public platforms without revealing sensitive data.

Contribution

It presents a novel integration of sliding window, Rabin-Karp, and zk-SNARKs for scalable, privacy-preserving string matching with practical implementation and experimental validation.

Findings

Achieves strong privacy guarantees with efficient computation.

Reduces time complexity compared to traditional methods.

Demonstrates scalability and practical applicability.

Abstract

We present a secure and efficient string-matching platform leveraging zk-SNARKs (Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge) to address the challenge of detecting sensitive information leakage while preserving data privacy. Our solution enables organizations to verify whether private strings appear on public platforms without disclosing the strings themselves. To achieve computational efficiency, we integrate a sliding window technique with the Rabin-Karp algorithm and Rabin Fingerprint, enabling hash-based rolling comparisons to detect string matches. This approach significantly reduces time complexity compared to traditional character-by-character comparisons. We implement the proposed system using gnark, a high-performance zk-SNARK library, which generates succinct and verifiable proofs for privacy-preserving string matching. Experimental results demonstrate that our solution achieves strong privacy guarantees while maintaining computational efficiency and scalability. This work highlights the practical applications of zero-knowledge proofs in secure data verification and contributes a scalable method for privacy-preserving string matching.