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

doi:10.1109/BigData.2018.8621861

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 proceeding › Conference Paper › Other › peer-review

5 Citations (Scopus)

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 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 language	English
Title of host publication	Proceedings - 2018 IEEE International Conference on Big Data
Editors	Naoki 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 Publication	Piscataway NJ USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	5277-5278
Number of pages	2
ISBN (Electronic)	9781538650356, 9781538650349
ISBN (Print)	9781538650363
DOIs	https://doi.org/10.1109/BigData.2018.8621861
Publication status	Published - 2019
Externally published	Yes
Event	IEEE International Conference on Big Data (Big Data) 2018 - Seattle, United States of America Duration: 10 Dec 2018 → 13 Dec 2018 https://ieeexplore.ieee.org/xpl/conhome/8610059/proceeding (Proceedings) https://cci.drexel.edu/bigdata/bigdata2018/ (Website)

Conference

Conference	IEEE International Conference on Big Data (Big Data) 2018
Abbreviated title	IEEE BigData 2018
Country/Territory	United States of America
City	Seattle
Period	10/12/18 → 13/12/18
Internet address	https://ieeexplore.ieee.org/xpl/conhome/8610059/proceeding (Proceedings) https://cci.drexel.edu/bigdata/bigdata2018/ (Website)

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10.1109/BigData.2018.8621861

Cite this

Agarwal, A., Dowsley, R., McKinney, N. D., Wu, D., Lin, C.-T., Cock, M. D., & Nascimento, A. (2019). Privacy-preserving linear regression for brain-computer interface applications. In N. Abe, H. Liu, C. Pu, X. Hu, N. Ahmed, M. Qiao, Y. Song, D. Kossmann, B. Liu, K. Lee, J. Tang, J. He, & J. Saltz (Eds.), Proceedings - 2018 IEEE International Conference on Big Data (pp. 5277-5278). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/BigData.2018.8621861

Agarwal, Anisha ; Dowsley, Rafael ; McKinney, Nicholas D. et al. / Privacy-preserving linear regression for brain-computer interface applications. Proceedings - 2018 IEEE International Conference on Big Data. editor / Naoki 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. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2019. pp. 5277-5278

@inproceedings{135c7045e73a4be286f59ba9a4864438,

title = "Privacy-preserving linear regression for brain-computer interface applications",

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 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.",

author = "Anisha Agarwal and Rafael Dowsley and McKinney, {Nicholas D.} and Dongrui Wu and Chin-Teng Lin and Cock, {Martine De} and Anderson Nascimento",

year = "2019",

doi = "10.1109/BigData.2018.8621861",

language = "English",

isbn = "9781538650363",

pages = "5277--5278",

editor = "Abe, {Naoki } and Liu, {Huan } and Pu, {Calton } and Hu, {Xiaohua } and Ahmed, {Nesreen } and Qiao, {Mu } and Song, {Yang } and Kossmann, {Donald } and Liu, {Bing } and Lee, {Kisung } and Tang, {Jiliang } and He, {Jingrui } and Jeffrey Saltz",

booktitle = "Proceedings - 2018 IEEE International Conference on Big Data",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States of America",

note = "IEEE International Conference on Big Data (Big Data) 2018, IEEE BigData 2018 ; Conference date: 10-12-2018 Through 13-12-2018",

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}

Agarwal, A, Dowsley, R, McKinney, ND, Wu, D, Lin, C-T, Cock, MD & Nascimento, A 2019, Privacy-preserving linear regression for brain-computer interface applications. in N Abe, H Liu, C Pu, X Hu, N Ahmed, M Qiao, Y Song, D Kossmann, B Liu, K Lee, J Tang, J He & J Saltz (eds), Proceedings - 2018 IEEE International Conference on Big Data. IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 5277-5278, IEEE International Conference on Big Data (Big Data) 2018, Seattle, Washington, United States of America, 10/12/18. https://doi.org/10.1109/BigData.2018.8621861

Privacy-preserving linear regression for brain-computer interface applications. / Agarwal, Anisha; Dowsley, Rafael; McKinney, Nicholas D. et al.
Proceedings - 2018 IEEE International Conference on Big Data. ed. / Naoki 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. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, 2019. p. 5277-5278.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other › peer-review

TY - GEN

T1 - Privacy-preserving linear regression for brain-computer interface applications

AU - Agarwal, Anisha

AU - Dowsley, Rafael

AU - McKinney, Nicholas D.

AU - Wu, Dongrui

AU - Lin, Chin-Teng

AU - Cock, Martine De

AU - Nascimento, Anderson

PY - 2019

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AB - 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.

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EP - 5278

BT - Proceedings - 2018 IEEE International Conference on Big Data

A2 - Abe, Naoki

A2 - Liu, Huan

A2 - Pu, Calton

A2 - Hu, Xiaohua

A2 - Ahmed, Nesreen

A2 - Qiao, Mu

A2 - Song, Yang

A2 - Kossmann, Donald

A2 - Liu, Bing

A2 - Lee, Kisung

A2 - Tang, Jiliang

A2 - He, Jingrui

A2 - Saltz, Jeffrey

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Piscataway NJ USA

T2 - IEEE International Conference on Big Data (Big Data) 2018

Y2 - 10 December 2018 through 13 December 2018

ER -

Agarwal A, Dowsley R, McKinney ND, Wu D, Lin CT, Cock MD et al. Privacy-preserving linear regression for brain-computer interface applications. In Abe N, Liu H, Pu C, Hu X, Ahmed N, Qiao M, Song Y, Kossmann D, Liu B, Lee K, Tang J, He J, Saltz J, editors, Proceedings - 2018 IEEE International Conference on Big Data. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2019. p. 5277-5278 doi: 10.1109/BigData.2018.8621861

Privacy-preserving linear regression for brain-computer interface applications

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Protecting privacy of users in brain-computer interface applications

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