PYthon For Homomorphic Encryption Libraries, perform encrypted computations such as sum, mult, scalar product or matrix multiplication in Python, with NumPy compatibility. Uses SEAL/PALISADE as backends, implemented using Cython.

Privacy-Preserving Convolutional Neural Networks using Homomorphic Encryption

Federated learning using https://www.mdpi.com/1996-1073/16/6/2837 as base with Flower framework. A secure aggregation using Pyfhel was added.

Original SEAL homomorphic encryption library, slightly modified to use it as dependency for Pyfhel.

application that uses homomorphic encryption and Luhn's algorithm to validate credit card numbers without exposing actual card details. This provides a privacy-preserving method to verify sensitive information without compromising security.

Pyfhel_Restful_Api

Code and demonstrations from my BSc dissertation: Privacy-Preserving Data Analysis via Homomorphic Encryption (BFV & CKKS using Pyfhel)

encrypt client for pyfhel and cp-abe

more organized,better structure

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This document provides instructions for setting up Pyfhel, a Python library for Homomorphic Encryption (HE). Pyfhel allows performing arithmetic operations on encrypted data without needing to decrypt it first, enabling privacy-preserving computations.

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Python library for Addition, Subtraction, Multiplication and Scalar Product over encrypted integers (BFV/BGV schemes) and approximated floating point values (CKKS scheme).

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Towards Complementing Federated Learning by Homomorphic Encryption

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Using Pyfhel for the fully homomorphic encryption tool for database

SecBic-CCA: Secured Biclusterings - Cheng and Church Algorithm using CKKS scheme with Pyfhel Libary over gene expression data sets.

A series of homomorphically biclustering algorithms (SecBic) -- Bi-Correlation Clustering Algorithm -- using CKKS scheme with Pyfhel Libary over gene expression data sets

This project extends the traditional Battleship rules to a two-player, turn-based system in which both boards are stored and processed in encrypted form. All hit/miss evaluations are carried out homomorphically using Pyfhel’s BFV integer scheme.

O experimento demonstra como processar dados sensíveis sem acessá-los. O Cliente criptografa seu salário com a Pyfhel e envia o "ruído" matemático ao Servidor. Este aplica operações (como cálculo de bônus) diretamente no dado cifrado e devolve o resultado. O Cliente então usa sua chave privada para revelar o valor final. Privacidade total.