# mmFHE: mmWave Sensing with End-to-End Fully Homomorphic Encryption

arXiv: https://arxiv.org/abs/2603.22437 · HTML: https://mlsanity.com/paper/2603.22437/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 60 references that are in the map.
- First posted on arXiv 23 Mar 2026.

## Builds on (most cited first, 10 of 10)

- [THE-X: Privacy-Preserving Transformer Inference with Homomorphic Encryption](https://mlsanity.com/paper/2206.00216/) (2022)
- [TenSEAL: A Library for Encrypted Tensor Operations Using Homomorphic Encryption](https://mlsanity.com/paper/2104.03152/) (2021)
- [Towards Domain-Independent and Real-Time Gesture Recognition Using mmWave Signal](https://mlsanity.com/paper/2111.06195/) (2021)
- [NeuJeans: Private Neural Network Inference with Joint Optimization of Convolution and FHE Bootstrapping](https://mlsanity.com/paper/2312.04356/) (2023)
- [Efficient Ranking, Order Statistics, and Sorting under CKKS](https://mlsanity.com/paper/2412.15126/) (2024)
- [Danger of using fully homomorphic encryption: A look at Microsoft SEAL](https://mlsanity.com/paper/1906.07127/) (2019)
- [SoK: Secure Human-centered Wireless Sensing](https://mlsanity.com/paper/2211.12087/) (2022)
- [MIMOCrypt: Multi-User Privacy-Preserving Wi-Fi Sensing via MIMO Encryption](https://mlsanity.com/paper/2309.00250/) (2023)
- [Quantized Approximate Signal Processing (QASP): Towards Homomorphic Encryption for audio](https://mlsanity.com/paper/2505.10500/) (2025)
- [PrivyWave: Privacy-Aware Wireless Sensing of Heartbeat](https://mlsanity.com/paper/2511.02993/) (2025)

## Abstract (arXiv)

We present mmFHE, the first system that executes the entire cloud-side mmWave sensing pipeline including the DSP and ML inference under fully homomorphic encryption (FHE). mmFHE encrypts range profiles on an edge device after lightweight plaintext preprocessing and executes the entire mmWave signal-processing and ML inference pipeline homomorphically on a semi-honest cloud that operates exclusively on ciphertexts. At the core of mmFHE is a library of seven composable, data-oblivious FHE kernels that replace standard DSP routines with fixed arithmetic circuits for different application-specific pipelines. We demonstrate this approach on two representative tasks: vital-sign monitoring and gesture recognition. We formally prove two cryptographic guarantees for any pipeline assembled from this library: input privacy and data obliviousness. These guarantees effectively neutralize various supervised and unsupervised privacy attacks on raw data, including re-identification and data-dependent privacy leakage. Evaluation on three public radar datasets shows that encryption introduces negligible error versus the plaintext pipeline, with 84.5% gesture accuracy (vs. 84.7%). End-to-end cloud GPU latency is 1.21 s per 10 s vital-sign window and 5.76 s per 3 s gesture window. These results establish the initial feasibility of end-to-end mmWave sensing under FHE on commodity hardware.