GPSSE: A GPU-Accelerated Dynamic SSE Scheme with Efficient Batch Updating
Jiancong Zhou, Xiaojie Zhu, Yong Li, Shuguang Yuan, Chi Chen · 2024
Dynamic Searchable Symmetric Encryption (DSSE) allows cloud users to securely retrieve and update their data while outsourcing it to untrusted cloud service providers. Although extensive research efforts in recent years have notably improved the retrieval efficiency of DSSE, there remains potential for enhancing update efficiency, particularly in large-scale dataset updating scenarios. Therefore, we proposed a pioneering scheme called GPSSE (GPU-Accelerated Dynamic Searchable Symmetric Encryption Scheme) to explore accelerating batch updates of DSSE through GPU. In our design, we break the traditional chain-based data structure and build independent label-based data blocks to store entries. It facilitates the parallel updating of entries. Moreover, GPSSE achieves forward privacy and Type II backward privacy. In addition, we formally prove the security of the proposed GPSSE and show its practicality by conducting experiments using the publicly well-known Enron Email dataset. Experimental results demonstrate that GPSSE outperforms 40× to 194156× than other state-of-the-art schemes in updating efficiency.