Published: Feb. 3, 2025
Language: Английский
Published: Feb. 3, 2025
Language: Английский
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
(
Language: Английский
Citations
104IACR 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
Citations
8Sensors, 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
Citations
33The Journal of Supercomputing, Journal Year: 2023, Volume and Issue: 79(13), P. 15098 - 15139
Published: April 14, 2023
Language: Английский
Citations
33Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 325 - 345
Published: Jan. 1, 2024
Language: Английский
Citations
16Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 3 - 33
Published: Jan. 1, 2024
Language: Английский
Citations
11Published: Feb. 3, 2025
Language: Английский
Citations
2Published: 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
Citations
22Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 322 - 353
Published: Jan. 1, 2024
Language: Английский
Citations
8Artificial 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
Citations
1