ARK: Fully Homomorphic Encryption Accelerator with Runtime Data Generation and Inter-Operation Key Reuse

Kim, Jongmin; Lee, Gwangho; Kim, Sangpyo; Sohn, Gina; Kim, John; Rhu, Minsoo; Ahn, Jung Ho

doi:10.1109/micro56248.2022.00086

Cited by 33 publications

(8 citation statements)

References 65 publications

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“…However, F1 has a maximum polynomial degree support of only 16K, whereas RPU has no such limitations. More recent HE accelerators such as CraterLake [38], BTS [23], and ARK [22] target high multiplicative depth applications, but require large on-chip memories, e.g., 256MB for Craterlake. Craterlake supports up to 64K polynomial degree.…”

Section: Related Workmentioning

confidence: 99%

“…Recent studies have focused on enhancing hybrid protocols through improved neural architecture design [5,6,13,20], protocol optimization [6,21,27,29], and hardware acceleration [18,22,23,36,38]. State-of-the-art PI protocols break the entire PI process into two phases, a pre-processing (or offline) phase and an online phase, to move expensive computations offline and improve online inference latency.…”

Section: Introductionmentioning

confidence: 99%

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Towards Fast and Scalable Private Inference

Garimella

Neda

et al. 2023

Proceedings of the 20th ACM International Conference on Computing Frontiers

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Privacy and security have rapidly emerged as first order design constraints. Users now demand more protection over who can see their data (confidentiality) as well as how it is used (control). Here, existing cryptographic techniques for security fall short: they secure data when stored or communicated but must decrypt it for computation. Fortunately, a new paradigm of computing exists, which we refer to as privacy-preserving computation (PPC). Emerging PPC technologies can be leveraged for secure outsourced computation or to enable two parties to compute without revealing either users' secret data. Despite their phenomenal potential to revolutionize user protection in the digital age, the realization has been limited due to exorbitant computational, communication, and storage overheads.This paper reviews recent efforts on addressing various PPC overheads using private inference (PI) in neural network as a motivating application. First, the problem and various technologies, including homomorphic encryption (HE), secret sharing (SS), garbled circuits (GCs), and oblivious transfer (OT), are introduced. Next, a characterization of their overheads when used to implement PI is covered. The characterization motivates the need for both GCs and HE accelerators. Then two solutions are presented: HAAC for accelerating GCs and RPU for accelerating HE. To conclude, results and effects are shown with a discussion on what future work is needed to overcome the remaining overheads of PI. CCS CONCEPTS• Security and privacy → Privacy-preserving protocols; • Computer systems organization → Architectures.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards Fast and Scalable Private Inference

Garimella

Neda

et al. 2023

Proceedings of the 20th ACM International Conference on Computing Frontiers

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“…FHE accelerators for second-generation schemes have mostly been built after a classical CPU architecture [25,36,52]. They include a control unit that executes an instruction set, together with a set of arithmetic Processing Elements (PEs) that support different operations, e.g.…”

Section: Streaming Processormentioning

confidence: 99%

“…Of these dedicated implementations, GPU-based FHE accelerators are easiest to develop, but they typically only provide modest speedups [5,15,35,58]. At the other end of the spectrum, ASIC emulations in advanced technology nodes promise better FHE acceleration [25,36,37,52,53]. However, it can take years for these ASICs to be fabricated and become available [44], and they are typically specialized for a limited range of parameter sets.…”

Section: Introductionmentioning

confidence: 99%

FPT: A Fixed-Point Accelerator for Torus Fully Homomorphic Encryption

Van Beirendonck,

D'Anvers,

Turan

et al. 2023

Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security

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Fully Homomorphic Encryption is a technique that allows computation on encrypted data. It has the potential to drastically change privacy considerations in the cloud, but high computational and memory overheads are preventing its broad adoption. TFHE is a promising Torus-based FHE scheme that heavily relies on bootstrapping, the noise-removal tool that must be invoked after every encrypted gate computation.We present FPT, a Fixed-Point FPGA accelerator for TFHE bootstrapping. FPT is the first hardware accelerator to heavily exploit the inherent noise present in FHE calculations. Instead of double or single-precision floating-point arithmetic, it implements TFHE bootstrapping entirely with approximate fixed-point arithmetic. Using an in-depth analysis of noise propagation in bootstrapping FFT computations, FPT is able to use noise-trimmed fixed-point representations that are up to 50% smaller than prior implementations using floating-point or integer FFTs.FPT's microarchitecture is built as a streaming processor inspired by traditional streaming DSPs: it instantiates high-throughput computational stages that are directly cascaded, with simplified control logic and routing networks. We explore different throughputbalanced compositions of streaming kernels with a user-configurable streaming width in order to construct a full bootstrapping pipeline. FPT's streaming approach allows 100% utilization of arithmetic units and requires only small bootstrapping key caches, enabling an entirely compute-bound bootstrapping throughput of 1 BS / 35𝜇s. This is in stark contrast to the established classical CPU approach to FHE bootstrapping acceleration, which tends to be heavily memory and bandwidth-constrained.FPT is fully implemented and evaluated as a bootstrapping FPGA kernel for an Alveo U280 datacenter accelerator card. FPT achieves almost three orders of magnitude higher bootstrapping throughput than existing CPU-based implementations, and 2.5× higher throughput compared to recent ASIC emulation experiments.

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“…To enhance FHE scheme performance, researchers have been exploring custom hardware accelerators using ASIC and FPGA technologies. ASIC solutions [ 10 , 11 , 12 , 13 ] show promise, as they surpass CPU/GPU implementations and bridge the performance gap between plaintext and ciphertext computations. However, to accommodate large on-chip memory, expensive advanced technology nodes such as 7 nm or 12 nm are required for ASIC implementations.…”

Section: Introductionmentioning

confidence: 99%

Pipelined Key Switching Accelerator Architecture for CKKS-Based Fully Homomorphic Encryption

Duong-Ngoc

Lee

2023

Sensors

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The increasing ubiquity of big data and cloud-based computing has led to increased concerns regarding the privacy and security of user data. In response, fully homomorphic encryption (FHE) was developed to address this issue by enabling arbitrary computation on encrypted data without decryption. However, the high computational costs of homomorphic evaluations restrict the practical application of FHE schemes. To tackle these computational and memory challenges, a variety of optimization approaches and acceleration efforts are actively being pursued. This paper introduces the KeySwitch module, a highly efficient and extensively pipelined hardware architecture designed to accelerate the costly key switching operation in homomorphic computations. Built on top of an area-efficient number-theoretic transform design, the KeySwitch module exploited the inherent parallelism of key switching operation and incorporated three main optimizations: fine-grained pipelining, on-chip resource usage, and high-throughput implementation. An evaluation on the Xilinx U250 FPGA platform demonstrated a 1.6× improvement in data throughput compared to previous work with more efficient hardware resource utilization. This work contributes to the development of advanced hardware accelerators for privacy-preserving computations and promoting the adoption of FHE in practical applications with enhanced efficiency.

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ARK: Fully Homomorphic Encryption Accelerator with Runtime Data Generation and Inter-Operation Key Reuse

Cited by 33 publications

References 65 publications

Towards Fast and Scalable Private Inference

Towards Fast and Scalable Private Inference

FPT: A Fixed-Point Accelerator for Torus Fully Homomorphic Encryption

Pipelined Key Switching Accelerator Architecture for CKKS-Based Fully Homomorphic Encryption

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