Official repository of "Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Data"
-
Updated
Oct 16, 2024 - Python
Official repository of "Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Data"
Implementation for Obfuscation Networks
Project for CSC 533: Privacy. GDPR and CCPA compliance web Analytic Tool
This repository contains the code and data for the text anonymization enhancement method presented in B. Manzanares-Salor, D. Sánchez, Enhancing text anonymization via re-identification risk-based explainability, Submitted, 2024.
a prototype blockchain for storing health data privately in a distributed manner
[IJCAI'24 AISafety] Low-Latency Privacy-Preserving Deep Learning Design via Secure MPC
A Privacy-Preserving Framework Based on TensorFlow
Anonymize your Pandas data. Preserve privacy.
The formal implementation for IMWUT24 paper: PrivateGaze: Preserving User Privacy in Black-box Mobile Gaze Tracking Services
This repository contains the code and data for the text re-identification attack presented in B. Manzanares-Salor, D. Sánchez, P. Lison, Evaluating the disclosure risk of anonymized documents via a machine learning-based re-identification attack, Data Mining and Knowledge Discovery, 2024.
Official PyTorch implementation of Patch SplitNN (WACV2023)
A naïve implementation of a feature-transformation biometric template protection scheme based on simple XOR comparison. This is not meant for practical use, but for educational purposes as an introduction to BTP.
Secured Cheng and Church Algorithm performs encrypted computations such as sum, or matrix multiplication in Python for biclustering algorithm
Repository of the Artifical Intelligence of Knoxly
Playing with different Private Set Intersection protocols
FedAnil++ is a Privacy-Preserving and Communication-Efficient Federated Deep Learning Model to address non-IID data, privacy concerns, and communication overhead. This repo hosts a simulation for FedAnil++ written in Python.
FedAnil+ is a novel lightweight, and secure Federated Deep Learning Model to address non-IID data, privacy concerns, and communication overhead. This repo hosts a simulation for FedAnil+ written in Python.
The implementation of the "Robust Federated Learning by Mixture of Experts" study.
Add a description, image, and links to the privacy-preserving topic page so that developers can more easily learn about it.
To associate your repository with the privacy-preserving topic, visit your repo's landing page and select "manage topics."