Client-optimized algorithms and acceleration for encrypted compute offloading

Hagen, McKenzie van der; Lucia, Brandon

doi:10.1145/3503222.3507737

Cited by 9 publications

(2 citation statements)

References 48 publications

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“…Natarajan et al introduced SEAL-Embedded, the first HE library specifically designed for embedded devices, featuring the CKKS-based approximate HE scheme in 2021 [6]. Hagen et al presented CHOCO, a client-optimized system for encrypted offload processing that supports both B/FV and CKKS HE schemes, in 2022 [7]. Recently, Azad et al proposed RACE, a custom-designed, area-and energy-efficient RISC-V SoC for the data ENC-DEC using CKKS HE, in 2022 [8].…”

Section: Related Workmentioning

confidence: 99%

Configurable Encryption and Decryption Architectures for CKKS-Based Homomorphic Encryption

Lee,

Duong,

Lee

2023

Sensors

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With the increasing number of edge devices connecting to the cloud for storage and analysis, concerns about security and data privacy have become more prominent. Homomorphic encryption (HE) provides a promising solution by not only preserving data privacy but also enabling meaningful computations on encrypted data; while considerable efforts have been devoted to accelerating expensive homomorphic evaluation in the cloud, little attention has been paid to optimizing encryption and decryption (ENC-DEC) operations on the edge. In this paper, we propose efficient hardware architectures for CKKS-based ENC-DEC accelerators to facilitate computations on the client side. The proposed architectures are configurable to support a wide range of polynomial sizes with multiplicative depths (up to 30 levels) at a 128-bit security guarantee. We evaluate the hardware designs on the Xilinx XCU250 FPGA platform and achieve an average encryption time 23.7× faster than that of the well-known SEAL HE library. By reducing time complexity and improving the hardware utilization of cryptographic algorithms, our configurable CKKS-supported ENC-DEC hardware designs have the potential to greatly accelerate cryptographic processes on the client side in the post-quantum era.

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Section: Related Workmentioning

confidence: 99%

Configurable Encryption and Decryption Architectures for CKKS-Based Homomorphic Encryption

Lee,

Duong,

Lee

2023

Sensors

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“…However, the above accelerators do not consider this traffic. CHOCO [64] shows that, even after accelerating client operations, communication costs dominate.…”

Section: Additional Related Workmentioning

confidence: 99%

CraterLake

Samardzic

Feldmann

Krastev

et al. 2022

Proceedings of the 49th Annual International Symposium on Computer Architecture

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Fully Homomorphic Encryption (FHE) enables offloading computation to untrusted servers with cryptographic privacy. Despite its attractive security, FHE is not yet widely adopted due to its prohibitive overheads, about 10,000× over unencrypted computation. Recent FHE accelerators have made strides to bridge this performance gap. Unfortunately, prior accelerators only work well for simple programs, but become inefficient for complex programs, which bring additional costs and challenges.We present CraterLake, the first FHE accelerator that enables FHE programs of unbounded size (i.e., unbounded multiplicative depth). Such computations require very large ciphertexts (tens of MBs each) and different algorithms that prior work does not support well. To tackle this challenge, CraterLake introduces a new hardware architecture that efficiently scales to very large ciphertexts, novel functional units to accelerate key kernels, and new algorithms and compiler techniques to reduce data movement.We evaluate CraterLake on deep FHE programs, including deep neural networks like ResNet and LSTMs, where prior work takes minutes to hours per inference on a CPU. CraterLake outperforms a CPU by gmean 4,600× and the best prior FHE accelerator by 11.2× under similar area and power budgets. These speeds enable realtime performance on unbounded FHE programs for the first time. CCS CONCEPTS• Computer systems organization → Parallel architectures; • Security and privacy → Cryptography.

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