Privacy-Preserving Linear Regression for Brain-Computer Interface Applications

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

  • Anisha Agarwal, University of Washington Tacoma
  • ,
  • Rafael Dowsley
  • ,
  • Nicholas D. McKinney, University of Washington Tacoma
  • ,
  • Dongrui Wu, Huazhong University of Science and Technology
  • ,
  • Chin Teng Lin, University of Technology, Sydney
  • ,
  • Martine De Cock, University of Washington Tacoma, Universiteit Gent
  • ,
  • Anderson Nascimento, University of Washington Tacoma

Many machine learning (ML) applications rely on large amounts of personal data for training and inference. Among the most intimate exploited data sources is electroencephalogram (EEG) data. The emergence of consumer-grade, low-cost brain-computer interfaces (BCIs) and corresponding software development kits1 is bringing the use of BCI within reach of application developers. The access that BCI applications have to neural signals rightly raises privacy concerns. Application developers can easily gain knowledge beyond the professed scope from unprotected EEG signals, including passwords, ATM PINs, and other personal data [1]. The challenge we address is how to engage in meaningful ML with EEG data while protecting the privacy of users.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
EditorsYang Song, Bing Liu, Kisung Lee, Naoki Abe, Calton Pu, Mu Qiao, Nesreen Ahmed, Donald Kossmann, Jeffrey Saltz, Jiliang Tang, Jingrui He, Huan Liu, Xiaohua Hu
Number of pages2
Publication year22 Jan 2019
Article number8621861
ISBN (Electronic)9781538650356
Publication statusPublished - 22 Jan 2019
Event2018 IEEE International Conference on Big Data, Big Data 2018 - Seattle, United States
Duration: 10 Dec 201813 Dec 2018


Conference2018 IEEE International Conference on Big Data, Big Data 2018
LandUnited States
SponsorBaidu, et al, Expedia Group, IEEE, IEEE Computer Society, Squirrel AI Learning

See relations at Aarhus University Citationformats

ID: 164770761