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fully homomorphic encryption

topic3 events
papersTODAY 04:00 UTC

EI-DDLGN: Encrypted Inference with Differentiable Logic Gate Networks under TFHE

A new arXiv preprint introduces EI-DDLGN, a framework for privacy-preserving deep learning inference built on Torus Fully Homomorphic Encryption (TFHE). The authors note that most existing TFHE-compatible neural network designs rely on arithmetic neurons, and their approach instead uses deep differentiable logic gate networks to improve efficiency. The work targets outsourced inference scenarios where sensitive input data must stay encrypted.

privacy/homomorphic-encryption/research/efficiency
papersSEP 11 04:00 UTC

mmFHE Runs Whole mmWave Sensing Pipeline Under Homomorphic Encryption

A new arXiv paper introduces mmFHE, a system that performs an entire cloud-side mmWave sensing workflow, including signal processing and machine learning inference, on encrypted data using fully homomorphic encryption. Range profiles are encrypted on the edge device first, so the cloud never sees raw sensing data. The work aims to enable privacy-preserving sensing services without giving up cloud compute.

privacy/homomorphic-encryption/mmwave/research
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