Ares: High Performance Near-Storage Accelerator for FHE-based Private Set Intersection

Nowadays, the importance of data privacy protection has grown significantly. Privacy Set Intersection (PSI) based on Fully Homomorphic Encryption (FHE) is widely applied in various privacy protection scenarios, such as federated learning and password verification. Nevertheless, the substantial computational demands of FHE and the vast scale of databases in PSI result in inefficient processing, thereby necessitating specialized accelerator architectures to enhance usability. Current general-purpose FHE accelerators do not adequately address the unique requirements of PSI applications, leading to suboptimal data handling and underutilization of hardware, which impedes their effective deployment for PSI acceleration. This paper introduces Ares, a practical hardware-software co-designed FHE-based PSI FPGA accelerator. We propose Lazy Relinearization to optimize redundant calculations in PSI and reduce computational complexity without changing the PSI protocol. At the same time, through the analysis and decoupling of the PSI computing pattern, we design an efficient hardware acceleration architecture that fully utilizes the bandwidth and computing resources of the hardware to achieve excellent acceleration performance. We highlight the following result: (1) a $47.99 \times$ speedup relative to CPU; (2) performance improvements of $1.79 \times$ and $1.93 \times$ over the state-of-theart FPGA FHE accelerators, Poseidon and FAB, respectively; (3) achieves $7.96 \times$ and $10.95 \times$ energy efficiency improvement compared to Poseidon and FAB, respectively.