Fully Homomorphic Encryption (FHE) enables computation over encrypted data, but it faces significant challenges in practical implementation due to its high computational costs, particularly in HMult, HRot, and Bootstrapping operations. This work presents Athena, an accelerated FHE system built on GPUs with a new algorithm-hardware co-design approach. Specifically, to accelerate HMult, HRot, and Bootstrapping, we redesign their common and expensive operation KeySwitch, based on the KLSS method proposed by Kim et al. in CRYPTO’23, and accelerate its core operations, namely NTT, EBConv, and IP. We further optimize the dataflow of Bootstrapping by reducing redundant EBConv and (I)NTT operations, and by improving the global memory I/O in the double-hoisting-based C2S/S2C operation. Moreover, Athena is designed as a general-purpose system that supports various cryptographic parameters. Experimental results demonstrate that Athena significantly improves the performance of KeySwitch and Bootstrapping. In particular, Athena’s accelerated KeySwitch optimizes HMult 2.17\times \sim 4.40\times and HRot 1.89\times \sim 4.54\times compared to TensorFHE (HPCA’23), Poseidon (HPCA’23), and FAB (HPCA’23), respectively. Besides, Athena’s Bootstrapping outperforms TensorFHE by nearly 2.74\times .

The honey password vault is a promising method for managing user passwords and mitigating password-guessing attacks by creating plausible-looking decoy password vaults. Recently, various methods, such as Chatterjee-PCFG (IEEE S&P’15), Golla-Markov (ACM CCS’16), and Cheng-IUV (USENIX Security’21), have been proposed to construct the cornerstone of honey password vaults, known as the distribution transforming encoder (DTE). These innovations significantly enhance the security and functionality of each kind of DTE. However, our findings indicate that when users employ multiple honey password vaults of distinct DTEs to manage their passwords, a passive attacker can easily compromise user passwords by exploiting differences among those DTEs. Consequently, we propose the differential attack targeting existing honey password vaults. The extensive experimental results confirm the effectiveness of this attack, distinguishing real from decoy password vaults with accuracy from 99.13% to 100.00%. In response, we design a novel, collaborative approach to train DTE, called federated DTE model, and construct a secure honey password vault. This strategy markedly bolsters security, reducing the differential attack's distinguishing accuracy to approximately 52.41%, nearing the ideal threshold of 50.00%. Our findings emphasize the need for collaborative strategies to maintain password security to combat advanced cyber threats.