Collateral Learning - Functional Encryption and Adversarial Training on partially encrypted networks
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Updated
Jul 25, 2024 - Jupyter Notebook
Collateral Learning - Functional Encryption and Adversarial Training on partially encrypted networks
A lightweight Key-Policy Attribute-Based Encryption scheme in C++
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Python implementation of some existing functional encryption schemes for the inner product functionality
Celestia proxy service enabling Private Blockspace
Implementation of Few Interesting Functional Encryption Schemes
A collection of research and survey papers of differential privacy and federated learning
PyFE4AI is a research-oriented Python library for functional encryption in trustworthy AI systems.
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A Pythonic-Sage implementation of the Ciphertext Updatable Functional Encryption (CUFE) scheme based on works of Valerio Cini et al
GPU-accelerated functional encryption with CUDA BSGS and validated MNIST benchmarks.
GPU-accelerated functional encryption with CUDA BSGS and validated MNIST benchmarks.
Browser-based inner-product functional encryption demo — ABDP15 (PKC 2015) over ristretto255. A key releasing only ⟨x, y⟩ beside one releasing the whole vector, the baby-step giant-step bottleneck measured in group operations, exact reconstruction from enough keys. Issue one key too many and you hand over the master secret.
Python proof of concept implementation of the SPADE Functional encryption scheme.
WIP: Practical implementation of ABE (Attribute-Based Encryption) in TypeScript. Currently focusing on CP-ABE.
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