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Module Learning with Errors with Truncated Matrices
The Module Learning with Errors (MLWE) problem is one of the most commonly used hardness assumption in lattice-based cryptography. In its standard version, a matrix A is sampled uniformly at random over a quotient ring Rq, as well as noisy linear equations in the form of As+emodq, where s is the secret, sampled uniformly at random over Rq, and e is theBoudgoust, Katharina, Keller, Hannah
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Collusion Resistant Traitor Tracing from Learning with Errors
SIAM Journal on Computing, 2018zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Goyal, Rishab +2 more
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Learning with Errors over Rings
2010The “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.
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Compact Ring Signatures from Learning with Errors
2021Ring 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. +7 more
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Learning with Errors in the Exponent
2016The Snowden revelations have shown that intelligence agencies have been successful in undermining cryptography and put in question the exact security provided by the underlying intractability problem. We introduce a new class of intractability problems, called Learning with Errors in the Exponent (LWEE).
Özgür Dagdelen +2 more
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Error-Driven Learning with Bracketing Constraints
2006A chunking algorithm with a Markov model is extended to accept bracketing constraints. The extended algorithm is implemented by modifying a state-of-the-art Japanese dependency parser. Then the effect of bracketing constraints in preventing parsing errors is evaluated. A method for improving the parser’s accuracy is proposed.
Takashi Miyata, Kôiti Hasida
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Active learning with error-correcting output codes
Neurocomputing, 2019Abstract In many real-world classification problems, while there is a large amount of unlabeled data, labeled data is usually hard to acquire. One way to solve these problems is active learning. It aims to select the most valuable instances for labeling and construct a superior classifier.
Shilin Gu +3 more
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THE ROLE OF ERRORS IN LEARNING WITH FEEDBACK
British Journal of Educational Psychology, 1966S ummary . The extent to which errors interfere with efficient learning was examined in two experiments in which punchboards were used to provide immediate feedback.
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Learning With Errors Parameter Analysis
We implement a systematic approach for generating, evaluating, and benchmarking Learning with Errors implementations in Sage Math by varying lattice dimensions, moduli, error standard deviations, and multiple error distributions to observe concrete security-efficiency tradeoffs. The security estimator maps parameter sets to concrete security levels andopenaire +1 more source
Analysis of Error Terms of Signatures Based on Learning with Errors
2017Lyubashevsky proposed a lattice-based digital signature scheme based on short integer solution SIS problem without using trapdoor matrices [12]. Bai and Galbraith showed that the hard problem in Lyubashevsky's scheme can be changed from SIS to SIS and learning with errors LWE [4]. Using this change, they could compress the signatures.
Jeongsu Kim +6 more
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