Results 231 to 240 of about 243,777 (279)

Graph Neural Networks Model Based on Atomic Hybridization for Predicting Drug Targets. [PDF]

J Chem Inf Model
Mohamed A, Galal N, Brooks BR, Amin M.
europepmc   +1 more source

A Fiducial-Marker-Based Localization Method for Automotive Chassis Bolt Assembly. [PDF]

Sensors (Basel)
Peng X, Xiao Y, Chen Z, Chen K, Huang H.
europepmc   +1 more source

Environmental dynamics shape human learning: change points versus random walks

Foucault C, Weber LA, Hunt L.
europepmc   +1 more source

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SLAP: Simpler, Improved Private Stream Aggregation from Ring Learning with Errors

Journal of Cryptology, 2023
Information or data aggregation helps in many real-life settings to acquire or improve knowledge of the problem(s) at hand, in particular nowadays in the world of overwhelming internet-based time-series data streams. In order to master the analysis of multiple-user time-series data streams while maintaining user/data holder privacy efficiently, private
Takeshita, Jonathan, Karl, Ryan, Gong, Ting, Jung, Taeho   +3 more
openaire   +1 more source

Compact Ring Signatures from Learning with Errors

2021
Ring signatures allow a user to sign a message on behalf of a “ring” of signers, while hiding the true identity of the signer. As the degree of anonymity guaranteed by a ring signature is directly proportional to the size of the ring, an important goal in cryptography is to study constructions that minimize the size of the signature as a function of ...
Chatterjee R., Garg S., Hajiabadi M., Khurana D., Liang X., Malavolta G., Pandey O., Shiehian S.   +7 more
openaire   +2 more sources

Ring Learning with Errors Cryptography

2020
In this chapter, we will discuss Ring-Learning with Errors cryptography (RLWE) as one of the most powerful and challenging approaches for developing professional and complex applications and systems.
Marius Iulian Mihailescu, Stefania Loredana Nita   +1 more
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On Ideal Lattices and Learning with Errors over Rings

Journal of the ACM, 2010
The “learning with errors” (LWE) problem is to distinguish random linear equations, which have been perturbed by a small amount of noise, from truly uniform ones. The problem has been shown to be as hard as worst-case lattice problems, and in recent years it has served as the foundation for a plethora of cryptographic applications. Unfortunately, these
Lyubashevsky, Vadim, Peikert, Chris, Regev, Oded   +2 more
openaire   +1 more source