Privacy-preserving linear regression for brain-computer interface applications

Anisha Agarwal, Rafael Dowsley, Nicholas D. McKinney, Dongrui Wu, Chin-Teng Lin, Martine De Cock, Anderson Nascimento

Research output: Chapter in Book/Report/Conference proceedingConference PaperOtherpeer-review

3 Citations (Scopus)


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
EditorsNaoki Abe, Huan Liu, Calton Pu, Xiaohua Hu, Nesreen Ahmed, Mu Qiao, Yang Song, Donald Kossmann, Bing Liu, Kisung Lee, Jiliang Tang, Jingrui He, Jeffrey Saltz
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages2
ISBN (Electronic)9781538650356, 9781538650349
ISBN (Print)9781538650363
Publication statusPublished - 2019
Externally publishedYes
EventIEEE International Conference on Big Data (Big Data) 2018 - Seattle, United States of America
Duration: 10 Dec 201813 Dec 2018 (Proceedings) (Website)


ConferenceIEEE International Conference on Big Data (Big Data) 2018
Abbreviated titleIEEE BigData 2018
Country/TerritoryUnited States of America
Internet address

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