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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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

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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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On the Precision Loss in Approximate Homomorphic Encryption

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 325 - 345

Published: Jan. 1, 2024

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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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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Efficient and secure content-based image retrieval with deep neural networks in the mobile cloud computing

Computers & Security, Journal Year: 2023, Volume and Issue: 128, P. 103163 - 103163

Published: Feb. 27, 2023

Language: Английский

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Securing Approximate Homomorphic Encryption Using Differential Privacy

Lecture notes in computer science, Journal Year: 2022, Volume and Issue: unknown, P. 560 - 589

Published: Jan. 1, 2022

Language: Английский

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Scalable and Privacy-Preserving Federated Principal Component Analysis

2022 IEEE Symposium on Security and Privacy (SP), Journal Year: 2023, Volume and Issue: unknown

Published: May 1, 2023

Principal component analysis (PCA) is an essential algorithm for dimensionality reduction in many data science domains. We address the problem of performing a federated PCA on private distributed among multiple providers while ensuring confidentiality. Our solution, SF-PCA, end-to-end secure system that preserves confidentiality both original and all intermediate results passive-adversary model with up to all-but-one colluding parties. SF-PCA jointly leverages multiparty homomorphic encryption, interactive protocols, edge computing efficiently interleave computations local cleartext operations collectively encrypted data. obtains as accurate non-secure centralized solutions, independently distribution It scales linearly or better dataset dimensions number providers. more precise than existing approaches approximate solution by combining results, between 3x 250x faster privacy-preserving alternatives based solely computation encryption. work demonstrates practical applicability datasets.

Language: Английский

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Homomorphic Encryption based Federated Learning for Financial Data Security

Published: March 16, 2023

Federated Learning is a distributed machine learning technique that enables on-device training without exchanging the sensitive data over centralized server. In this paper, used to train financial models on-devices with help of IoT applications in or business systems. This creates more advanced and secured models. We have applied mechanism homomorphic encryption cryptographic primitives, including masking local model protection prevent any kind inferring private where multiple attackers usually find way inversion reconstruction attack. datasets various sectors as primary measure, rather than taking size generally deep learning, get correct measurement rate contribution every session model's global model. If calculated online number clients exceeds predetermined threshold, then federated process will be continued dropout-tolerant plan. The security study demonstrates suggested solution fulfils privacy requirements. costs computation communication are also examined theoretically. According research observations, proposed approach achieved promising outcomes while assuring preservation when compared existing schemes.

Language: Английский

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