SoK: Fully Homomorphic Encryption Compilers
Alexander Viand, Patrick Jattke, Anwar Hithnawi
摘要
Fully Homomorphic Encryption (FHE) allows a third party to perform arbitrary computations on encrypted data, learning neither the inputs nor the computation results. Hence, it provides resilience in situations where computations are carried out by an untrusted or potentially compromised party. This powerful concept was first conceived by Rivest et al. in the 1970s. However, it remained unrealized until Craig Gentry presented the first feasible FHE scheme in 2009.The advent of the massive collection of sensitive data in cloud services, coupled with a plague of data breaches, moved highly regulated businesses to increasingly demand confidential and secure computing solutions. This demand, in turn, has led to a recent surge in the development of FHE tools. To understand the landscape of recent FHE tool developments, we conduct an extensive survey and experimental evaluation to explore the current state of the art and identify areas for future development.In this paper, we survey, evaluate, and systematize FHE tools and compilers. We perform experiments to evaluate these tools’ performance and usability aspects on a variety of applications. We conclude with recommendations for developers intending to develop FHE-based applications and a discussion on future directions for FHE tools development.
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引用它的顶会 Paper22
- SPIRAL: Fast, High-Rate Single-Server PIR via FHE CompositionSamir Jordan Menon, David J. WuS&P 2022 · 被引用 153 次
- ZeeStar: Private Smart Contracts by Homomorphic Encryption and Zero-knowledge ProofsSamuel Steffen, Benjamin Bichsel, Roger Baumgartner, Martin T. VechevS&P 2022 · 被引用 64 次
- Zeph: Cryptographic Enforcement of End-to-End Data PrivacyLukas Burkhalter, Nicolas Küchler, Alexander Viand, Hossein Shafagh 等OSDI 2021 · 被引用 35 次
- MAGE: Nearly Zero-Cost Virtual Memory for Secure ComputationSam Kumar, David E. Culler, Raluca Ada PopaOSDI 2021 · 被引用 24 次
- A Tensor Compiler with Automatic Data Packing for Simple and Efficient Fully Homomorphic EncryptionAleksandar Krastev, Nikola Samardzic, Simon Langowski, Srinivas Devadas 等PLDI 2024 · 被引用 23 次
它引用的顶会 Paper9
- GAZELLE: A Low Latency Framework for Secure Neural Network InferenceChiraag Juvekar, Vinod Vaikuntanathan, Anantha P. ChandrakasanUSENIX Security 2018 · 被引用 1,075 次
- Labeled PSI from Fully Homomorphic Encryption with Malicious SecurityHao Chen, Zhicong Huang, Kim Laine, Peter RindalCCS 2018 · 被引用 242 次
- Efficient Multi-Key Homomorphic Encryption with Packed Ciphertexts with Application to Oblivious Neural Network InferenceHao Chen, Wei Dai, Miran Kim, Yongsoo SongCCS 2019 · 被引用 235 次
- SoK: General Purpose Compilers for Secure Multi-Party ComputationMarcella Hastings, Brett Hemenway, Daniel Noble, Steve ZdancewicS&P 2019 · 被引用 181 次
- On the Security of Homomorphic Encryption on Approximate NumbersBaiyu Li, Daniele MicciancioEUROCRYPT 2021 · 被引用 165 次
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