# Protecting Privacy of Users in Brain-Computer Interface Applications

arXiv: https://arxiv.org/abs/1907.01586 · HTML: https://mlsanity.com/paper/1907.01586/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2019.

## Built on by (most cited first, 13 of 13)

- [Privacy-Preserving Feature Selection with Secure Multiparty Computation](https://mlsanity.com/paper/2102.03517/) (2021)
- [Domain-Invariant Representation Learning from EEG with Private Encoders](https://mlsanity.com/paper/2201.11613/) (2022)
- [Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces](https://mlsanity.com/paper/2412.01079/) (2024)
- [Revisiting Euclidean Alignment for Transfer Learning in EEG-Based Brain-Computer Interfaces](https://mlsanity.com/paper/2502.09203/) (2025)
- [Secure Multiparty Computation for Synthetic Data Generation from Distributed Data](https://mlsanity.com/paper/2210.07332/) (2022)
- [Privacy-Preserving Training of Tree Ensembles over Continuous Data](https://mlsanity.com/paper/2106.02769/) (2021)
- [Reputation-Based Federated Learning Defense to Mitigate Threats in EEG Signal Classification](https://mlsanity.com/paper/2401.01896/) (2023)
- [Protecting Multiple Types of Privacy Simultaneously in EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/2411.19498/) (2024)
- [Fast Privacy-Preserving Text Classification based on Secure Multiparty Computation](https://mlsanity.com/paper/2101.07365/) (2021)
- [Training Differentially Private Models with Secure Multiparty Computation](https://mlsanity.com/paper/2202.02625/) (2022)
- [Round and Communication Balanced Protocols for Oblivious Evaluation of Finite State Machines](https://mlsanity.com/paper/2103.11240/) (2021)
- [SecureV2X: An Efficient and Privacy-Preserving System for Vehicle-to-Everything (V2X) Applications](https://mlsanity.com/paper/2508.19115/) (2025)
- [NeuroPriv: Adversarial Representation Learning for Privacy in Wearable EEG Systems](https://mlsanity.com/paper/2609.00390/) (2026)

## Abstract (arXiv)

Machine learning (ML) is revolutionizing research and industry. Many ML applications rely on the use of large amounts of personal data for training and inference. Among the most intimate exploited data sources is electroencephalogram (EEG) data, a kind of data that is so rich with information that application developers can easily gain knowledge beyond the professed scope from unprotected EEG signals, including passwords, ATM PINs, and other intimate data. The challenge we address is how to engage in meaningful ML with EEG data while protecting the privacy of users. Hence, we propose cryptographic protocols based on Secure Multiparty Computation (SMC) to perform linear regression over EEG signals from many users in a fully privacy-preserving (PP) fashion, i.e.~such that each individual's EEG signals are not revealed to anyone else. To illustrate the potential of our secure framework, we show how it allows estimating the drowsiness of drivers from their EEG signals as would be possible in the unencrypted case, and at a very reasonable computational cost. Our solution is the first application of commodity-based SMC to EEG data, as well as the largest documented experiment of secret sharing based SMC in general, namely with 15 players involved in all the computations.