🧹 Formerly for binary classification with noisy labels. Replaced by cleanlab.
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Updated
May 15, 2022 - Jupyter Notebook
🧹 Formerly for binary classification with noisy labels. Replaced by cleanlab.
Post-quantum key exchange from the learning with errors problem — from the paper "Frodo: Take off the ring! Practical, Quantum-Secure Key Exchange from LWE", published in ACM CCS 2016, https://eprint.iacr.org/2016/659
practical quantum-secure key encapsulation from generic lattices
Python implementation of "Somewhat Practical Fully Homomorphic Encryption"
This project proposes the use of plain lattices with learning with errors problem to implement a cryptographic scheme which can run on classical computers and provides security against quantum based attacks. We are proposing key sizes for efficient operations and implement a lattice trapdoor function. Also we will improve current random oracle b…
Prototypes of a Learning With Errors (LWE) Implementation
Fast and ergonomic lattice cryptography library
Python implementation of "Fully Homomorphic Encryption without Modulus Switching from Classical GapSVP"
FrodoKEM: Practical Quantum-secure Key Encapsulation from Generic Lattices
crypto fun repository
Some experiments to empirically analyze how the parameters of LWE impact the correctness of the algorithm on a single bit.
Privacy-Preserving E-Health Record Management Using Blockchain-Based Post-Quantum Access Control https://ieeexplore.ieee.org/document/10794736 doi:10.1109/UNet62310.2024.10794736 #CPABE #crypto #cryptography #smartcontracts #ieee #blockchain #ethereum #ehealth #python #flask #solidity #truffle #postquantumcryptography
Module-Lattice-Based Key Encapsulation Mechanism (FIPS-203) implemented in python
Artifacts for ePrint 2025/1002 "Cool + Cruel = Dual, and New Benchmarks for Sparse LWE
R&D environment to study Cryptography so we can roll our own eventually
An educational framework and simulation tool exploring the Entropic Threat Continuum, combining Regev's LWE cryptography, Shannon entropy for mixnets, and Differential Privacy composition bounds.
A Python implementation of the Learning with Errors Algorithm
Implementations and proofs-of-concepts of cryptographic attacks
https://ieeexplore.ieee.org/document/10794713 doi:10.1109/UNet62310.2024.10794713 #MAC #KeyExchangeProtocol #MessageAuthenticationCode #Authentication #LearningWithErrors #LWE #KEP #Cryptography #Cryptanalysis #Validity #Correctness #postquantumcryptography
Experimental implementation of a lattice-based cryptosystem using Multi-Block Learning With Errors (LWE) and basic Hamming (7,4) error correction. This project stage is not production-ready and exhibits notable decoding errors. Designed for educational purposes, algorithm exploration, collaboration to improve post-quantum cryptography methods.
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