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

---

OpenFHE

Published: Nov. 1, 2022

Fully Homomorphic Encryption (FHE) is a powerful cryptographic primitive that enables performing computations over encrypted data without having access to the secret key. We introduce OpenFHE, new open-source FHE software library incorporates selected design ideas from prior projects, such as PALISADE, HElib, and HEAAN, includes several concepts ideas. The main features can be summarized follows: (1) we assume very beginning all implemented schemes will support bootstrapping scheme switching; (2) OpenFHE supports multiple hardware acceleration backends using standard Hardware Abstraction Layer (HAL); (3) both user-friendly modes, where maintenance operations, modulus switching, key bootstrapping, are automatically invoked by library, compiler-friendly an external compiler makes these decisions. This paper focuses on high-level description of design, reader pointed references for more detailed/technical library.

Language: Английский

---

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

---

CraterLake

Published: May 31, 2022

Fully Homomorphic Encryption (FHE) enables offloading computation to untrusted servers with cryptographic privacy. Despite its attractive security, FHE is not yet widely adopted due prohibitive overheads, about 10,000X over unencrypted computation. Recent accelerators have made strides bridge this performance gap. Unfortunately, prior only work well for simple programs, but become inefficient complex which bring additional costs and challenges.

Language: Английский

---

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

---

FAB: An FPGA-based Accelerator for Bootstrappable Fully Homomorphic Encryption

Published: Feb. 1, 2023

Fully Homomorphic Encryption (FHE) offers protection to private data on third-party cloud servers by allowing computations the in encrypted form. To support general-purpose computations, all existing FHE schemes require an expensive operation known as "bootstrapping". Unfortunately, computation cost and memory bandwidth required for bootstrapping add significant overhead FHE-based limiting practical use of FHE.In this work, we propose FAB, FPGA-based accelerator bootstrappable FHE. Prior accelerators have proposed hardware acceleration basic primitives impractical parameter sets without bootstrapping. first time ever, accelerates (along with primitives) FPGA a secure set. The key contribution work is architecture balanced FAB design, which not bound. In our leverage recent algorithms while being cognizant compute constraints FPGA. addition, minimal number functional units computing, operate at low frequency, high rates from main memory, utilize limited on-chip effectively, perform careful scheduling.We evaluate using single Xilinx Alveo U280 scaling it multi-FPGA system consisting eight such FPGAs. For fully-packed ciphertext, operating 300MHz, outperforms state-of-the-art CPU GPU implementations 213× 1.5× respectively. Our target application training logistic regression model over data. scaled 8 FPGAs cloud, 456× 9.5× respectively, providing performance fraction ASIC design cost.

Language: Английский

---

Security Guidelines for Implementing Homomorphic Encryption

IACR Communications in Cryptology, Journal Year: 2025, Volume and Issue: 1(4)

Published: Jan. 13, 2025

Fully Homomorphic Encryption (FHE) is a cryptographic primitive that allows performing arbitrary operations on encrypted data. Since the conception of idea in [RAD78], it has been considered holy grail cryptography. After first construction 2009 [Gen09], evolved to become practical with strong security guarantees. Most modern constructions are based well-known lattice problems such as Learning With Errors (LWE). Besides its academic appeal, recent years FHE also attracted significant attention from industry, thanks applicability considerable number real-world use-cases. An upcoming standardization effort by ISO/IEC aims support wider adoption these techniques. However, one main challenges standards bodies, developers, and end users usually encounter establishing parameters. This particularly hard case because parameters not only related level system, but type system able handle. In this paper we provide examples parameter sets for LWE targeting particular levels, can be used context constructions. We give complete sets, including relevant correctness performance, alongside those security. As an additional contribution, survey selection offered open-source libraries.

Language: Английский

---

POSEIDON: Privacy-Preserving Federated Neural Network Learning

Published: Jan. 1, 2021

Furthermore, the trusted party becomes a single point of failure, thus both data and model privacy could be compromised by breaches, hacking, leaks, etc.Hence, solutions originating from cryptographic community replace emulate with group computing servers.In particular, to enable privacy-preserving training NNs, several studies employ multiparty computation (MPC) techniques operate on two [83], [28], three [82], [110],[111], or four [26], [27] server models.Such approaches, however, limit number parties among which trust is split, often assume an honest majority servers, require communicate (i.e., secret share) their outside premises.This might not acceptable due confidentiality requirements strict protection regulations.Furthermore, servers do own benefit training; hence, only incentive reputation harm if they are caught, increases possibility malicious behavior.A recently proposed alternative for NNs -without outsourcing -is federated learning.Instead bringing model, brought (via coordinating server) clients, who perform updates local data.The updated models averaged obtain global NN [75], [63].Although learning retains sensitive input locally eliminates need outsourcing, that also sensitive, e.g., proprietary reasons, available server, placing latter in position power respect remaining parties.Recent research demonstrates sharing intermediate lead various attacks, such as extracting parties' inputs [53], [113], [120] membership inference [78], [86].Consequently, works differential exchanges values free adversarial inferences settings [67], [101], [76].Although differentially private partially attacks learning, decrease utility resulting ML model.Furthermore, robust accurate requires high budgets, such, level achieved practice remains unclear [55].Therefore, distributed deep approach strong during training, well final weights.Recent approaches [119], [42], have limited functionalities, i.e., regularized generalized linear models, but traditional encryption schemes make them vulnerable post-quantum attacks.This should cautiously considered, recent advances quantum [47], [87], [105], [116], increase deploying quantum-resilient eliminate Abstract-In this paper, we address problem privacypreserving evaluation neural networks N-party, setting.We propose novel system, POSEIDON, first its kind regime network training.It employs lattice-based cryptography preserve data, under passive-adversary collusions between up N -1 parties.To efficiently execute secure backpropagation algorithm networks, provide generic packing enables Single Instruction, Multiple Data (SIMD) operations encrypted data.We introduce arbitrary transformations within bootstrapping operation, optimizing costly computations over parties, define constrained optimization choosing parameters.Our experimental results show POSEIDON achieves accuracy similar centralized decentralized non-private communication overhead scales linearly parties.POSEIDON trains 3-layer MNIST dataset 784 features 60K samples 10 less than 2 hours.

Language: Английский

---

High-Precision Bootstrapping of RNS-CKKS Homomorphic Encryption Using Optimal Minimax Polynomial Approximation and Inverse Sine Function

Lecture notes in computer science, Journal Year: 2021, Volume and Issue: unknown, P. 618 - 647

Published: Jan. 1, 2021

Language: Английский

---

Multiparty Homomorphic Encryption from Ring-Learning-with-Errors

Proceedings on Privacy Enhancing Technologies, Journal Year: 2021, Volume and Issue: 2021(4), P. 291 - 311

Published: July 23, 2021

Abstract We propose and evaluate a secure-multiparty-computation (MPC) solution in the semi-honest model with dishonest majority that is based on multiparty homomorphic encryption (MHE). To support our solution, we introduce version of Brakerski-Fan-Vercauteren cryptosystem implement it an open-source library. MHE-based MPC solutions have several advantages: Their transcript public, their o~ine phase compact, circuit-evaluation procedure noninteractive. By exploiting these properties, communication complexity tasks reduced from quadratic to linear number parties, thus enabling secure computation among potentially thousands parties broad variety computing paradigms, traditional peer-to-peer setting cloud-outsourcing smart-contract technologies. approaches can also outperform state-of-the-art solutions, even for small parties. demonstrate this three circuits: private input selection application private-information retrieval, component-wise vector multiplication private-set intersection, Beaver triples generation . For first circuit, privately selecting one eight thousand parties’ (of 32 KB each) requires only 1.31 MB per party completes 61.7 seconds. second circuit approach 8.6 times faster 39.3 less than current methods. third ten generates 20 more while requiring 136 per-triple oblivious transfer. implemented scheme Lattigo library open-sourced code at github.com/ldsec/lattigo.

Language: Английский

---