Privacy-Preserving Machine Learning With Fully Homomorphic Encryption for Deep Neural Network

IEEE Access, Journal Year: 2022, Volume and Issue: 10, P. 30039 - 30054

Published: Jan. 1, 2022

Fully homomorphic encryption (FHE) is a prospective tool for privacy-preserving machine learning (PPML). Several PPML models have been proposed based on various FHE schemes and approaches. Although are suitable as tools implementing models, previous FHE, such CryptoNet, SEALion, CryptoDL, limited to simple nonstandard types of models; they not proven be efficient accurate with more practical advanced datasets. Previous replaced non-arithmetic activation functions arithmetic instead adopting approximation methods did use bootstrapping, which enables continuous evaluations. Thus, could neither standard nor employ large numbers layers. In this work, we first implement the ResNet-20 model RNS-CKKS bootstrapping verify implemented CIFAR-10 dataset plaintext parameters. Instead replacing functions, state-of-the-art evaluate these ReLU Softmax, sufficient precision. Further, time, technique scheme in model, us an arbitrary deep encrypted data. We numerically that shows 98.43% identical results original non-encrypted The classification accuracy 92.43%±2.65%, quite close CNN (91.89%). It takes approximately 3 h inference dual Intel Xeon Platinum 8280 CPU (112 cores) 172 GB memory. believe opens possibility applying model.

Language: Английский

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Survey on Fully Homomorphic Encryption, Theory, and Applications

Proceedings of the IEEE, Journal Year: 2022, Volume and Issue: 110(10), P. 1572 - 1609

Published: Oct. 1, 2022

Data privacy concerns are increasing significantly in the context of Internet Things, cloud services, edge computing, artificial intelligence applications, and other applications enabled by next-generation networks. Homomorphic encryption addresses challenges enabling multiple operations to be performed on encrypted messages without decryption. This article comprehensively homomorphic from both theoretical practical perspectives. delves into mathematical foundations required understand fully ( $\textsf {FHE}$ ). It consequently covers design fundamentals security properties describes main schemes based various problems. On a more level, this presents view privacy-preserving machine learning using then surveys at length an engineering angle, covering potential application fog computing services. also provides comprehensive analysis existing state-of-the-art libraries tools, implemented software hardware, performance thereof.

Language: Английский

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BTS

Published: May 31, 2022

Homomorphic encryption (HE) enables the secure offloading of computations to cloud by providing computation on encrypted data (ciphertexts). HE is based noisy schemes in which noise accumulates as more are applied data. The limited number operations applicable prevents practical applications from exploiting HE. Bootstrapping an unlimited or fully (FHE) refreshing ciphertext. Unfortunately, bootstrapping requires a significant amount additional and memory bandwidth well. Prior works have proposed hardware accelerators for primitives FHE. However, best our knowledge, this first propose FHE accelerator that supports first-class citizen.

Language: Английский

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Efficient Bootstrapping for Approximate Homomorphic Encryption with Non-sparse Keys

Lecture notes in computer science, Journal Year: 2021, Volume and Issue: unknown, P. 587 - 617

Published: Jan. 1, 2021

Language: Английский

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

Published: Oct. 1, 2022

Homomorphic Encryption (HE) is one of the most promising post-quantum cryptographic schemes that enable privacy-preserving computation on servers. However, noise accumulates as we perform operations HE-encrypted data, restricting number possible operations. Fully HE (FHE) removes this restriction by introducing bootstrapping operation, which refreshes data; however, FHE are highly memory-bound. Bootstrapping, in particular, requires loading GBs evaluation keys and plaintexts from offchip memory, makes acceleration fundamentally bottlenecked off-chip memory bandwidth.In paper, propose ARK, an Accelerator for with Runtime data generation inter-operation Key reuse. ARK enables practical workloads a novel algorithm-architecture co-design to accelerate bootstrapping. We first eliminate bandwidth bottleneck through runtime key This approach fully exploit on-chip substantially reducing size working set. On top such algorithmic enhancements, build microarchitecture minimizes movement efficient, alternating distribution policy based access patterns streamlined dataflow organization tailored functional units – including base conversion, number-theoretic transform, automorphism units. Overall, our codesign effectively handles heavy overheads FHE, drastically cost operations,

Language: Английский

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A Survey of Deep Learning Architectures for Privacy-Preserving Machine Learning With Fully Homomorphic Encryption

IEEE Access, Journal Year: 2022, Volume and Issue: 10, P. 117477 - 117500

Published: Jan. 1, 2022

Outsourced computation for neural networks allows users access to state-of-the-art models without investing in specialized hardware and know-how. The problem is that the lose control over potentially privacy-sensitive data. With homomorphic encryption (HE), a third party can perform on encrypted data revealing its content. In this paper, we reviewed scientific articles publications particular area of Deep Learning Architectures Privacy-Preserving Machine (PPML) with Fully HE. We analyzed changes network architectures make them compatible HE how these impact performance. Next, find numerous challenges HE-based privacy-preserving deep learning, such as computational overhead, usability, limitations posed by schemes. Furthermore, discuss potential solutions PPML challenges. Finally, propose evaluation metrics allow better more meaningful comparison solutions.

Language: Английский

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High-Precision Bootstrapping for Approximate Homomorphic Encryption by Error Variance Minimization

Lecture notes in computer science, Journal Year: 2022, Volume and Issue: unknown, P. 551 - 580

Published: Jan. 1, 2022

Language: Английский

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Approximate Homomorphic Encryption with Reduced Approximation Error

Lecture notes in computer science, Journal Year: 2022, Volume and Issue: unknown, P. 120 - 144

Published: Jan. 1, 2022

Language: Английский

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Poseidon: Practical Homomorphic Encryption Accelerator

Published: Feb. 1, 2023

With the development of important solution for privacy computing, explosion data size and computing intensity in Fully Homomorphic Encryption (FHE) has brought enormous challenges to hardware design. In this paper, we propose a practical FHE accelerator - "Poseidon", which focuses on improving resource bandwidth consumption. Poseidon supports complex operations like Bootstrapping, Keyswitch, Rotation so on, under limited FPGA resources. It refines these by abstracting five key operators: Modular Addition (MA), Multiplication (MM), Number Theoretic Transformation (NTT), Automorphsim Shared Barret Reduction (SBT). These operators are combined reused implement higher-level operations. To utilize resources more efficiently improve parallelism, adopt radix-based NTT algorithm HFAuto, an optimized automorphism implementation suitable FPGA. Then, design based HBM maximize computational efficiency. We evaluate with four domain-specific benchmarks Xilinx Alveo U280 Empirical results show that efficient reuse operator cores on-chip storage enables superior performance compared state-of-the-art GPU, ASICs. highlight following results: (1) up 370× speedup over CPU basic FHE; (2) 1300×/52× operators; (3) 10.6×/8.7× GPU ASIC benchmark.

Language: Английский

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SHARP: A Short-Word Hierarchical Accelerator for Robust and Practical Fully Homomorphic Encryption

Published: June 16, 2023

Fully homomorphic encryption (FHE) is an emerging cryptographic technology that guarantees the privacy of sensitive user data by enabling direct computations on encrypted data. Despite security benefits this approach, FHE associated with prohibitively high levels computational and memory overhead, preventing its widespread use in real-world services. Numerous domain-specific hardware designs have been proposed to address issue, but most them excessive amounts chip area power, leaving room for further improvements terms practicality.

Language: Английский

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