Matcha

Jiang, Lei; Lou, Qian; Joshi, Nrushad

doi:10.1145/3489517.3530435

Cited by 28 publications

(5 citation statements)

References 7 publications

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“…For example, Reis et al [ 62 ] proposed a computing-in-memory-based HE implementation to gain energy savings of between 266.4 times and 532.8 times for homomorphic multiplications (the most expensive HE operation) compared with a CPU-based HE solution. In addition, Lei et al [ 63 ] proposed an energy-efficient accelerator for fully homomorphic encryption that improves the throughput per Watt by 6.3 times compared with that of previous accelerators.…”

Section: Discussionmentioning

confidence: 99%

A Privacy-Preserving Framework Using Homomorphic Encryption for Smart Metering Systems

Sun

Cardell-Oliver

et al. 2023

Sensors

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Smart metering systems (SMSs) have been widely used by industrial users and residential customers for purposes such as real-time tracking, outage notification, quality monitoring, load forecasting, etc. However, the consumption data it generates can violate customers’ privacy through absence detection or behavior recognition. Homomorphic encryption (HE) has emerged as one of the most promising methods to protect data privacy based on its security guarantees and computability over encrypted data. However, SMSs have various application scenarios in practice. Consequently, we used the concept of trust boundaries to help design HE solutions for privacy protection under these different scenarios of SMSs. This paper proposes a privacy-preserving framework as a systematic privacy protection solution for SMSs by implementing HE with trust boundaries for various SMS scenarios. To show the feasibility of the proposed HE framework, we evaluated its performance on two computation metrics, summation and variance, which are often used for billing, usage predictions, and other related tasks. The security parameter set was chosen to provide a security level of 128 bits. In terms of performance, the aforementioned metrics could be computed in 58,235 ms for summation and 127,423 ms for variance, given a sample size of 100 households. These results indicate that the proposed HE framework can protect customer privacy under varying trust boundary scenarios in SMS. The computational overhead is acceptable from a cost–benefit perspective while ensuring data privacy.

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Section: Discussionmentioning

confidence: 99%

A Privacy-Preserving Framework Using Homomorphic Encryption for Smart Metering Systems

Sun

Cardell-Oliver

et al. 2023

Sensors

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“…FPT achieves a massive PBS throughput of 1 PBS / 35𝜇s, which requires only modest off-chip memory bandwidth, and is entirely bound by the logic resources on our target Xilinx Alveo U280 FPGA. This represents almost three orders of magnitude speedup over the popular TFHE software library CONCRETE [12] on an Intel Xeon Silver 4208 CPU at 2.1 GHz, a factor 7.1× speedup over a concurrently-developed FPGA architecture [62], and a factor 2.5× speedup over recent 16nm ASIC emulation experiments [33].…”

Section: Introductionmentioning

confidence: 91%

“…One prior implementation proposing a custom hardware format for TFHE's FFTs is MATCHA [33], who propose to use (integer) Dyadic-Value-Quantized Twiddle Factors (DVQTFs). Our fixed-point parameter analysis improves on MATCHA's in two key ways:…”

Section: Related and Future Workmentioning

confidence: 99%

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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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“…Strix [99] is an ASIC made for evaluating TFHE over streaming data at high throughput. They achieve over 1067x higher throughput compared to CPU implementation (Concrete), 37x higher throughput over the GPU implementation [100], and 7.4x over the state-of-the-art ASIC called Matcha [101], which was the first ASIC designed for TFHE. In Strix, authors identify that blind rotation operation during the PBS step runs sequentially, leading to significant performance degradation.…”

Section: Fpga and Asicsmentioning

confidence: 99%

High Performance Privacy Preserving AI

Shenoy,

Grinaway,

Palakodety

2024

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Homomorphic EncryptionHomomorphic encryption (HE) is one technique that enables privacy-preserving computation. Operations (or circuit evaluations) are performed directly on homomorphically encrypted data. The result can then be decrypted. Since its introduction 46 years ago, homomorphic encryption has evolved significantly with a plethora of schemes to choose from, each with their own advantages and disadvantages. Following the invention of fully homomorphic encryption (FHE) in 2009, there has been a revolution in the field. FHE theoretically allows for evaluation of arbitrary circuits of unbounded depth. HE can be used with a variety of models, ranging from simple support vector machines (SVMs) and random forests to computationally expensive deep neural networks.

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Matcha

Cited by 28 publications

References 7 publications

A Privacy-Preserving Framework Using Homomorphic Encryption for Smart Metering Systems

A Privacy-Preserving Framework Using Homomorphic Encryption for Smart Metering Systems

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

High Performance Privacy Preserving AI

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