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Microsoft SEAL comes with two different homomorphic encryption schemes with very different properties: BFV : [ 5 ] The BFV scheme allows modular arithmetic to be performed on encrypted integers. For applications where exact values are necessary, the BFV scheme is the only choice.
Meta-learning [1] [2] is a subfield of machine learning where automatic learning algorithms are applied to metadata about machine learning experiments. As of 2017, the term had not found a standard interpretation, however the main goal is to use such metadata to understand how automatic learning can become flexible in solving learning problems, hence to improve the performance of existing ...
Alice selects a binary (n, k)-linear Goppa code, G, capable of correcting t errors. This code possesses an efficient decoding algorithm. Alice generates a (n − k) × n parity check matrix, H, for the code, G. Alice selects a random (n − k) × (n − k) binary non-singular matrix, S. Alice selects a random n × n permutation matrix, P.
Encryption, by itself, can protect the confidentiality of messages, but other techniques are still needed to protect the integrity and authenticity of a message; for example, verification of a message authentication code (MAC) or a digital signature usually done by a hashing algorithm or a PGP signature.
The encryption scheme and the private key creation process are based on Boolean algebra. This technique has the advantage of small time and memory complexities. A disadvantage is the property of backpropagation algorithms: because of huge training sets, the learning phase of a neural network is very long.
[3] [6] [7] I.e., if there exists an algorithm that can efficiently break the cryptographic scheme with non-negligible probability, then there exists an efficient algorithm that solves a certain lattice problem on any input. However, for the practical lattice-based constructions (such as schemes based on NTRU and even schemes based on LWE with ...
In cryptography, learning with errors (LWE) is a mathematical problem that is widely used to create secure encryption algorithms. [1] It is based on the idea of representing secret information as a set of equations with errors. In other words, LWE is a way to hide the value of a secret by introducing noise to it. [2]
Meta-learning is a branch of metacognition concerned with learning about one's own learning and learning processes. The term comes from the meta prefix's modern meaning of an abstract recursion , or "X about X", similar to its use in metaknowledge , metamemory , and meta-emotion .