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HyFL: A Hybrid Framework For Private Federated Learning
Federated learning (FL) has emerged as an efficient approach for large-scale distributed machine learning, ensuring data privacy by keeping training data on client devices.
ScionFL: Efficient and Robust Secure Quantized Aggregation
In this paper, we unite both research directions by introducing ScionFL, the first secure aggregation framework for FL that operates efficiently on quantized inputs and simultaneously provides robustness against malicious clients.
Trustworthy AI Inference Systems: An Industry Research View
Additionally, such systems should also use Privacy-Enhancing Technologies (PETs) to protect customers' data at any time.
CryptoSPN: Privacy-preserving Sum-Product Network Inference
AI algorithms, and machine learning (ML) techniques in particular, are increasingly important to individuals' lives, but have caused a range of privacy concerns addressed by, e. g., the European GDPR.
Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications
Chameleon departs from the common assumption of additive or linear secret sharing models where three or more parties need to communicate in the online phase: the framework allows two parties with private inputs to communicate in the online phase under the assumption of a third node generating correlated randomness in an offline phase.
