Alternatives and similar repositories for anonypy:

Users that are interested in anonypy are comparing it to the libraries listed below

• IFCA-Advanced-Computing / pycanon
  pyCANON is a Python library and CLI to assess the values of the parameters associated with the most common privacy-preserving techniques.
  ☆36Updated last week
• qiyuangong / Mondrian
  Python Implementation for Mondrian Multidimensional K-Anonymity (Mondrian).
  ☆173Updated last year
• Nuclearstar / K-Anonymity
  Anonymization methods for network security.
  ☆157Updated 2 months ago
• kaylode / k-anonymity
  Evaluating variety of k-Anonymity techniques.
  ☆54Updated last year
• fhstp / k-AnonML
  ☆20Updated last year
• OpenMined / PyDP
  The Python Differential Privacy Library. Built on top of: https://github.com/google/differential-privacy
  ☆515Updated 7 months ago
• statice / anonymeter
  A Unified Framework for Quantifying Privacy Risk in Synthetic Data according to the GDPR
  ☆87Updated 2 months ago
• alan-turing-institute / tapas
  ☆36Updated last year
• opendp / opendp
  The core library of differential privacy algorithms powering the OpenDP Project.
  ☆354Updated this week
• opendp / smartnoise-sdk
  Tools and service for differentially private processing of tabular and relational data
  ☆265Updated 3 months ago
• spring-epfl / synthetic_data_release
  ☆39Updated 2 years ago
• privacytrustlab / ml_privacy_meter
  Privacy Meter: An open-source library to audit data privacy in statistical and machine learning algorithms.
  ☆647Updated last week
• hharcolezi / multi-freq-ldpy
  Multiple Frequency Estimation Under Local Differential Privacy in Python
  ☆47Updated last year
• Samuel-Maddock / pure-LDP
  Python package for simple implementations of state-of-the-art LDP frequency estimation algorithms. Contains code for our VLDB 2021 Paper.
  ☆74Updated last year
• IBM / differential-privacy-library
  Diffprivlib: The IBM Differential Privacy Library
  ☆862Updated this week
• ObliviousAI / petlab_competition
  🏁 The core repository to support participants through the UN PET Lab Hackathon 2022 👉 Registration at: https://petlab.officialstatistic…
  ☆19Updated 2 years ago
• ganevgv / dp-generative-models
  Differentially Private (tabular) Generative Models Papers with Code
  ☆48Updated 10 months ago
• microsoft / privGAN
  PrivGAN: Protecting GANs from membership inference attacks at low cost
  ☆33Updated 10 months ago
• menisadi / awesome-differential-privacy
  list of differential-privacy related resources
  ☆308Updated 3 months ago
• DPBayes / twinify
  A software package for privacy-preserving generation of a synthetic twin to a given sensitive data set.
  ☆52Updated 8 months ago
• leriomaggio / ppml-tutorial
  Privacy-Preserving Machine Learning (PPML) Tutorial
  ☆38Updated 11 months ago
• microsoft / dp-transformers
  Differentially-private transformers using HuggingFace and Opacus
  ☆136Updated 8 months ago
• mc2-project / federated-xgboost
  Federated gradient boosted decision tree learning
  ☆68Updated 2 years ago
• IBM / ai-privacy-toolkit
  A toolkit for tools and techniques related to the privacy and compliance of AI models.
  ☆102Updated 10 months ago
• hharcolezi / ldp-protocols-mobility-cdrs
  Implementation of local differential privacy mechanisms in Python language.
  ☆28Updated 2 years ago
• schneiderkamplab / syntheval
  Software for evaluating the quality of synthetic data compared with real data.
  ☆20Updated last month
• opendp / dpcreator
  Web application that makes data releases that satisfy differential privacy using the OpenDP Library
  ☆22Updated 9 months ago
• microsoft / analysing_pii_leakage
  The repository contains the code for analysing the leakage of personally identifiable (PII) information from the output of next word pred…
  ☆93Updated 8 months ago
• lxuechen / private-transformers
  A codebase that makes differentially private training of transformers easy.
  ☆170Updated 2 years ago
• ryan112358 / private-pgm
  An implementation of the tools described in the paper entitled "Graphical-model based estimation and inference for differential privacy"
  ☆102Updated this week