Accelerating LTV Based Homomorphic Encryption in Reconfigurable Hardware

Doröz, Yarkın; Öztürk, Erdinç; Savaş, Erkay; Sunar, Berk

doi:10.1007/978-3-662-48324-4_10

Cited by 23 publications

(27 citation statements)

References 29 publications

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“…In the literature there are several reported hardware implementation that try to speedup performance of HE schemes [11,12,13,14,15,16,17,18,19,20,21]. Several of these reported implementations report only simulation based results.…”

Section: Introductionmentioning

confidence: 99%

FPGA-Based High-Performance Parallel Architecture for Homomorphic Computing on Encrypted Data

Roy

Turan

Järvinen

et al. 2019

2019 IEEE International Symposium on High Performance Computer Architecture (HPCA)

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Homomorphic encryption is a tool that enables computation on encrypted data and thus has applications in privacy-preserving cloud computing. Though conceptually amazing, implementation of homomorphic encryption is very challenging and typically software implementations on general purpose computers are extremely slow. In this paper we present our domain specific architecture in a heterogeneous Arm+FPGA platform to accelerate homomorphic computing on encrypted data. We design a custom co-processor for the computationally expensive operations of the well-known Fan-Vercauteren (FV) homomorphic encryption scheme on the FPGA, and make the Arm processor a server for executing different homomorphic applications in the cloud, using this FPGA-based co-processor. We use the most recent arithmetic and algorithmic optimization techniques and perform design-space exploration on different levels of the implementation hierarchy. In particular we apply circuit-level and block-level pipeline strategies to boost the clock frequency and increase the throughput respectively. To reduce computation latency, we use parallel processing at all levels. Starting from the highly optimized building blocks, we gradually build our multicore multi-processor architecture for computing. We implemented and tested our optimized domain specific programmable architecture on Xilinx Zynq UltraScale+ MPSoC ZCU102 Evaluation Kit. At 200 MHz FPGAclock, our implementation achieves over 13x speedup with respect to a highly optimized software implementation of the FV homomorphic encryption scheme on an Intel i5 processor running at 1.8 GHz.

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Section: Introductionmentioning

confidence: 99%

FPGA-Based High-Performance Parallel Architecture for Homomorphic Computing on Encrypted Data

Roy

Turan

Järvinen

et al. 2019

2019 IEEE International Symposium on High Performance Computer Architecture (HPCA)

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“…Furthermore, in , the AES circuit is chosen to evaluate the homomorphic operations, which is faster than the proposed one in ]. Other improvements on hardware implementations of NTRU-like FHE schemes are more recently published in [Liu and Wu 2015;Doröz et al 2015b]. Another NTRU-like FHE scheme was suggested in .…”

Section: Lwe-based Fhe Schemesmentioning

confidence: 99%

A Survey on Homomorphic Encryption Schemes

et al. 2018

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Legacy encryption systems depend on sharing a key (public or private) among the peers involved in exchanging an encrypted message. However, this approach poses privacy concerns. The users or service providers with the key have exclusive rights on the data. Especially with popular cloud services, the control over the privacy of the sensitive data is lost. Even when the keys are not shared, the encrypted material is shared with a third party that does not necessarily need to access the content. Moreover, untrusted servers, providers, and cloud operators can keep identifying elements of users long after users end the relationship with the services. Indeed, Homomorphic Encryption (HE), a special kind of encryption scheme, can address these concerns as it allows any third party to operate on the encrypted data without decrypting it in advance. Although this extremely useful feature of the HE scheme has been known for over 30 years, the first plausible and achievable Fully Homomorphic Encryption (FHE) scheme, which allows any computable function to perform on the encrypted data, was introduced by Craig Gentry in 2009. Even though this was a major achievement, different implementations so far demonstrated that FHE still needs to be improved significantly to be practical on every platform. Therefore, this survey focuses on HE and FHE schemes. First, we present the basics of HE and the details of the well-known Partially Homomorphic Encryption (PHE) and Somewhat Homomorphic Encryption (SWHE), which are important pillars of achieving FHE. Then, the main FHE families, which have become the base for the other follow-up FHE schemes are presented. Furthermore, the implementations and recent improvements in Gentry-type FHE schemes are also surveyed. Finally, further research directions are discussed. This survey is intended to give a clear knowledge and foundation to researchers and practitioners interested in knowing, applying, as well as extending the state of the art HE, PHE, SWHE, and FHE systems.

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“…Therefore, a good number of works focused on developing FFT/NTT (Number Therotic Transform) based large integer multipliers. Currently, the only full-fledged (with bootstrapping) FHE hardware implementation is the one reported by [Doröz…15], which also implements the Gentry-Halevi FHE. At this time, there is a lack of hardware implementations of the more recently proposed FHE schemes, i.e.…”

Section: Related Workmentioning

confidence: 99%