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Private Machine Learning in TensorFlow using Secure Computation
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We present a framework for experimenting with secure multi-party computation directly in TensorFlow. By doing so we benefit from several properties valuable to both researchers and practitioners, including tight integration with ordinary machine learning processes, existing optimizations for distributed computation in TensorFlow, high-level abstractions for expressing complex algorithms and protocols, and an expanded set of familiar tooling. We give an open source implementation of a state-of-the-art protocol and report on concrete benchmarks using typical models from private machine learning.
Forward citations
Cited by 2 Pith papers
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MPC-Patch-Bench: Security-Aware LLM Code Patch for Multi-Party Computation
MPC-Patch-Bench supplies 205 curated MPC repository tasks and an MPC Verifier that drops LLM resolution rates from 22.9% functional to 17.1% security-verified.
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Covert Attacks on Machine Learning Training in Passively Secure MPC
An active adversary can exploit additive error injection in passively secure MPC training to poison models, amplify membership inference, reduce fairness, and reconstruct exact training data.
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