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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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

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A Review of Homomorphic Encryption for Privacy-Preserving Biometrics

Sensors, Journal Year: 2023, Volume and Issue: 23(7), P. 3566 - 3566

Published: March 29, 2023

The advancement of biometric technology has facilitated wide applications biometrics in law enforcement, border control, healthcare and financial identification verification. Given the peculiarity features (e.g., unchangeability, permanence uniqueness), security data is a key area research. Security privacy are vital to enacting integrity, reliability availability biometric-related applications. Homomorphic encryption (HE) concerned with manipulation cryptographic domain, thus addressing issues faced by biometrics. This survey provides comprehensive review state-of-the-art HE research context Detailed analyses discussions conducted on various approaches according categories different traits. Moreover, this presents perspective integrating other emerging technologies machine/deep learning blockchain) for security. Finally, based latest development biometrics, challenges future directions put forward.

Language: Английский

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A survey on implementations of homomorphic encryption schemes

The Journal of Supercomputing, Journal Year: 2023, Volume and Issue: 79(13), P. 15098 - 15139

Published: April 14, 2023

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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On the Practical $$\text {CPA}^{D}$$ Security of “exact” and Threshold FHE Schemes and Libraries

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 3 - 33

Published: Jan. 1, 2024

Language: Английский

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ReSBM: Region-based Scale and Minimal-Level Bootstrapping Management for FHE via Min-Cut

Published: Feb. 3, 2025

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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Faster Amortized FHEW Bootstrapping Using Ring Automorphisms

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 322 - 353

Published: Jan. 1, 2024

Language: Английский

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Approximate homomorphic encryption based privacy-preserving machine learning: a survey

Artificial Intelligence Review, Journal Year: 2025, Volume and Issue: 58(3)

Published: Jan. 6, 2025

Machine Learning (ML) is rapidly advancing, enabling various applications that improve people's work and daily lives. However, this technical progress brings privacy concerns, leading to the emergence of Privacy-Preserving (PPML) as a popular research topic. In work, we investigate protection topic in ML, showcase advantages Homomorphic Encryption (HE) among different privacy-preserving techniques. Additionally, presents an introduction approximate HE, emphasizing its providing detail some representative schemes. Moreover, systematically review related works about HE based PPML schemes from four three advanced applications, along with their application scenarios, models datasets. Finally, suggest potential future directions guide readers extending PPML.

Language: Английский

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