Privacy-preserving linear regression for brain-computer interface applications

Agarwal, A; Dowsley, R; McKinney, ND; Wu, D; Lin, C-T; Cock, MD; Nascimento, A

Privacy-preserving linear regression for brain-computer interface applications

Agarwal, A Dowsley, R McKinney, ND Wu, D Lin, C-T Cock, MD Nascimento, A

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings 2018 IEEE International Conference on Big Data, 2019
Issue Date:: 2019

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Field	Value	Language
dc.contributor.author	Agarwal, A
dc.contributor.author	Dowsley, R
dc.contributor.author	McKinney, ND
dc.contributor.author	Wu, D
dc.contributor.author	Lin, C-T
dc.contributor.author	Cock, MD
dc.contributor.author	Nascimento, A
dc.date	2018-12-10
dc.date.accessioned	2020-06-17T22:40:51Z
dc.date.available	2020-06-17T22:40:51Z
dc.date.issued	2019
dc.identifier.citation	Proceedings 2018 IEEE International Conference on Big Data, 2019
dc.identifier.isbn	978-1-5386-5035-6
dc.identifier.uri	http://hdl.handle.net/10453/141502
dc.description.abstract	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 kits' 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. The challenge is how to engage in meaningful ML with EEG data while protecting the privacy of users.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Proceedings 2018 IEEE International Conference on Big Data
dc.relation.ispartof	IEEE International Conference on Big Data
dc.relation.isbasedon	10.1109/bigdata.2018.8621861
dc.rights	© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Privacy-preserving linear regression for brain-computer interface applications
dc.type	Conference Proceeding
utslib.location.activity	Seattle, WA, USA
utslib.for	0803 Computer Software
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-06-17T22:40:38Z
pubs.finish-date	2018-12-13
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2018-12-10
dc.location	Piscataway, USA

Abstract:

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 kits' 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. The challenge is how to engage in meaningful ML with EEG data while protecting the privacy of users.

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http://hdl.handle.net/10453/141502