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secure-computation

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This article uses a simple use case involving a transaction between a vendor and a customer to illustrate the privacy-enhancing potential of oblivious transfer (OT) and to demonstrate how OT can be incorporated into a Python implementation of a web service by leveraging the otc library.

  • Updated Nov 4, 2020
  • Jupyter Notebook

A curated collection of privacy-preserving machine learning techniques, tools, and practical evaluations. Focuses on differential privacy, federated learning, secure computation, and synthetic data generation for implementing privacy in ML workflows.

  • Updated Jun 9, 2025

A browser-based application relying on homomorphic encryption allowing for ranked-choice voting with ranking choices from 1 to 10 in a way such that nobody, including the server, can know what the individual choices were! Only the result is publicly known. Uses the threshold-elgamal package: https://www.npmjs.com/package/threshold-elgamal.

  • Updated May 30, 2025
  • TypeScript

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