Parallel Cuckoo Hashing: Accelerating Secure Encrypted Data Search in Cloud Environments
Hongyang Lin, Jiabei Wang, Tiancheng Zhu, Yiwen Gao, Quan Fen Yang, Yongbin Zhou · 2025
Cuckoo hashing serves as a fundamental technique in various privacy-enhancing cryptographic primitives, such as Private Information Retrieval, Symmetric Searchable Encryption, owing to its excellent performance. However, achieving space-efficient Cuckoo hashing that maintains fast insertion and query operations, while facilitating its applications with customized design, remains highly challenging. In this work, we propose a multi-segment permutation-based Cuckoo hashing (MS-PCH) that can be efficiently parallelized on multi-threaded platforms, followed by a strategy for further parallelization over the hashing within single segments (MH-MS-PCH). To demonstrate its practical utility, we then investigate its application on encrypted data search by constructing full-fledged Public Key (Authenticated) Encryption with Keyword Search schemes (PCH(-MD)-PEKS and PCH(-MD)-PAEKS). We evaluate the performance of these schemes on a public dataset, showing that, with 16 segments and 4 hash functions, both PCH(-MD)-PEKS and PCH(-MD)-PAEKS outperforms the plain PEKS and PAEKS across index generation, query, and update. Notably, our optimized Cuckoo hashing achieves up to a 10 times improvement over plain cuckoo hashing, and our enhanced P(A)EKS schemes demonstrate approximately a 6 times improvement in index generation efficiency and a 335 times acceleration in query processing.