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Homomorphic Encryption

topic2 events
papersTODAY 04:00 UTC

Federated Learning Framework Combines Differential Privacy and Homomorphic Encryption

A new arXiv paper presents a federated learning framework designed to make collaborative training across distributed data safer. It combines dynamic differential privacy, a lightweight homomorphic encryption scheme, and asynchronous aggregation to reduce privacy risks while limiting computational overhead. The work is posted as a preprint and has not yet been peer reviewed.

federated-learning/privacy/differential-privacy/encryption
papersSEP 10 04:00 UTC

New Paper Studies Selective Homomorphic Inference for Efficient Private ML

A research paper on arXiv examines selective homomorphic inference, an approach to running machine learning on private data using fully homomorphic encryption. Instead of evaluating an entire input under encryption, which is computationally expensive, the method applies FHE only to the sensitive region of interest. The work aims to make privacy-preserving inference more efficient and practical.

privacy/homomorphic-encryption/machine-learning/research