research-article

Privacy risks emerging from the adoption of innocuous wearable sensors in the mobile environment

Authors:

Mani SrivastavaAuthors Info & Claims

CHI '11: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Pages 11 - 20

https://doi.org/10.1145/1978942.1978945

Published: 07 May 2011 Publication History

Abstract

Wearable sensors are revolutionizing healthcare and science by enabling capture of physiological, psychological, and behavioral measurements in natural environments. However, these seemingly innocuous measurements can be used to infer potentially private behaviors such as stress, conversation, smoking, drinking, illicit drug usage, and others. We conducted a study to assess how concerned people are about disclosure of a variety of behaviors and contexts that are embedded in wearable sensor data. Our results show participants are most concerned about disclosures of conversation episodes and stress - inferences that are not yet widely publicized. These concerns are mediated by temporal and physical context associated with the data and the participant's personal stake in the data. Our results provide key guidance on the extent to which people understand the potential for harm and data characteristics researchers should focus on to reduce the perceived harm from such datasets.

References

[1]

AliveTec. Activity and Heart Monitor. http://www.alivetec.com/products.htm.

[2]

AutoSense. AutoSense: A Wireless Sensor System to Quantify Personal Exposures to Psychosocial Stress and Addictive Substances in Natural Environments. http://sites.google.com/site/autosenseproject.

[3]

M. Barbaro, T. Zeller, and S. Hansell. A Face is Exposed for AOL Searcher No. 4417749. New York Times, 2006.

[4]

L. Barkhuus and A. Dey. Location-Based Services for Mobile Telephony: a Study of Users' Privacy Concerns. In Proc. Interact, 2003.

[5]

A. Brush, J. Krumm, and J. Scott. Exploring end user preferences for location obfuscation, location-based services, and the value of location. In ACM UbiComp, pages 95--104, 2010.

Digital Library

[6]

S. Consolvo, J. Jung, B. Greenstein, P. Powledge, G. Maganis, and D. Avrahami. The Wi-Fi privacy ticker: improving awareness & control of personal information exposure on Wi-Fi. In ACM UbiComp, pages 321--330, 2010.

Digital Library

[7]

S. Consolvo, D. McDonald, T. Toscos, M. Chen, J. Froehlich, B. Harrison, P. Klasnja, A. LaMarca, L. LeGrand, R. Libby, et al. Activity sensing in the wild: a field trial of ubifit garden. In ACM SIGCHI, 2008.

Digital Library

[8]

S. Consolvo, I. Smith, T. Matthews, A. LaMarca, J. Tabert, and P. Powledge. Location Disclosure to Social Relations: Why,When, & What People Want to Share. In ACM SIGCHI, 2005.

Digital Library

[9]

A. Dey and G. Abowd. Towards a Better Understanding of Context and Context-Awareness. In ACM SIGCHI workshop, 2000.

[10]

FieldStream. FieldStream: Network Data Services for Exposure Biology Studies in Natural Environments. http://www.fieldstream.org/.

[11]

R. Ganti, N. Pham, Y. Tsai, and T. Abdelzaher. PoolView: Stream Privacy for Grassroots Participatory Sensing. In ACM SenSys, 2008.

Digital Library

[12]

S. Guha, K. Plarre, D. Lissner, S. Mitra, B. Krishna, P. Dutta, and S. Kumar. AutoWitness: Locating and Tracking Stolen Property while Tolerating GPS and Radio Outages. In ACM SenSys, 2010.

Digital Library

[13]

S. Hansell. AOL Removes Search Data on Vast Group of Web Users. New York Times, 2006.

[14]

G. Iachello and G. Abowd. Privacy and proportionality: adapting legal evaluation techniques to inform design in ubiquitous computing. In ACM SIGCHI, pages 91--100, 2005.

Digital Library

[15]

G. Iachello, K. Truong, G. Abowd, G. Hayes, and M. Stevens. Prototyping and sampling experience to evaluate ubiquitous computing privacy in the real world. In ACM SIGCHI, pages 1009--1018, 2006.

Digital Library

[16]

W. Karim. Privacy Implications of Personal Locators: Why You Should Think Twice before Voluntarily Availing Yourself to GPS Monitoring, The. Wash. UJL & Pol'y, 14, 2004.

[17]

P. Klasnja, S. Consolvo, T. Choudhury, R. Beckwith, and J. Hightower. Exploring privacy concerns about personal sensing. Pervasive Computing, pages 176--183, 2009.

Digital Library

[18]

P. Klasnja, S. Consolvo, J. Jung, B. Greenstein, L. LeGrand, P. Powledge, and D. Wetherall. "When I am on Wi-Fi, I am fearless:" privacy concerns & practices in everydayWi-Fi use. In ACM SIGCHI, pages 1993--2002, 2009.

Digital Library

[19]

J. Krumm. Inference Attacks on Location Tracks. Pervasive Computing, 2007.

Digital Library

[20]

R. Lazarus. Stress and emotion: A new synthesis. Springer Publishing Company, 2006.

[21]

N. Li, T. Li, and S. Venkatasubramanian. t-Closeness: Privacy Beyond k-Anonymity and l-Diversity. In IEEE ICDE, 2007.

[22]

K. Lorincz, B. Chen, G. Challen, A. Chowdhury, S. Patel, P. Bonato, and M. Welsh. Mercury: A Wearable Sensor Network Platform for High-Fidelity Motion Analysis. In ACM SenSys, 2009.

Digital Library

[23]

A. Machanavajjhala, D. Kifer, J. Gehrke, and M. Venkitasubramaniam. l-diversity: Privacy beyond k-Anonymity. ACM TKDD, 2007.

Digital Library

[24]

C. Mancini, K. Thomas, Y. Rogers, B. Price, L. Jedrzejczyk, A. Bandara, A. Joinson, and B. Nuseibeh. From spaces to places: emerging contexts in mobile privacy. In ACM UbiComp, 2009.

Digital Library

[25]

D. McFarland. Respiratory Markers of Conversational Interaction. Journal of Speech, Language, and Hearing Research, 44(1), 2001.

[26]

M. Mun, S. Reddy, K. Shilton, N. Yau, J. Burke, D. Estrin, M. Hansen, E. Howard, R. West, and P. Boda. Peir, The Personal Environmental Impact Report, as a Platform for Participatory Sensing Systems Research. In ACM MobiSys, 2009.

Digital Library

[27]

A. Narayanan and V. Shmatikov. Robust De-Anonymization of Large Sparse Datasets. In IEEE Symp. on Security and Privacy, 2008.

Digital Library

[28]

A. Narayanan and V. Shmatikov. De-Anonymizing Social Networks. In IEEE Symp. on Security and Privacy, 2009.

Digital Library

[29]

S. Oliveira and O. Zaiane. Privacy Preserving Clustering by Data Transformation. J. Information & Data Management, 1(1), 2010.

[30]

L. Palen and P. Dourish. Unpacking Privacy for a Networked World. In ACM SIGCHI, 2003.

Digital Library

[31]

S. Patil and J. Lai. Who gets to know what when: configuring privacy permissions in an awareness application. In ACM SIGCHI, 2005.

Digital Library

[32]

L. Sweeney. Achieving k-anonymity Privacy Protection Using Generalization and Suppression. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(5), 2002.

Digital Library

[33]

E. Toch, J. Cranshaw, P. Drielsma, J. Tsai, P. Kelley, J. Springfield, L. Cranor, J. Hong, and N. Sadeh. Empirical Models of Privacy in Location Sharing. In ACM UbiComp, 2010.

Digital Library

[34]

E. Toch, N. Sadeh, and J. Hong. Generating default privacy policies for online social networks. In ACM SIGCHI extended abstracts, pages 4243--4248, 2010.

Digital Library

[35]

J. Tsai, P. Kelley, P. Drielsma, L. Cranor, J. Hong, and N. Sadeh. Who's viewed you?: the impact of feedback in a mobile location-sharing application. In ACM SIGCHI, 2009.

Digital Library

[36]

Zephyr Technology. BioHarness BT. http://www.zephyr-technology.com/bioharness-bt.html.

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https://dl.acm.org/doi/10.1145/3653710
Ghosh SMandi SSen SMitra BDe P(2024)Towards Estimating Missing Emotion Self-reports Leveraging User Similarity: A Multi-task Learning ApproachProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642833(1-19)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642833
Lee HJung YLaw HBae SLee U(2024)PriviAware: Exploring Data Visualization and Dynamic Privacy Control Support for Data Collection in Mobile Sensing ResearchProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642815(1-17)Online publication date: 11-May-2024
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Privacy risks emerging from the adoption of innocuous wearable sensors in the mobile environment

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Published In

cover image ACM Conferences

CHI '11: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

May 2011

3530 pages

ISBN:9781450302289

DOI:10.1145/1978942

General Chair:
Desney Tan
Microsoft Research
,
Program Chairs:
Geraldine Fitzpatrick
Vienna University of Technology
,
Carl Gutwin
University of Saskatchewan
,
Bo Begole
PARC
,
Wendy A. Kellogg
IBM Research

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 07 May 2011

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CHI '11

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CHI '11: CHI Conference on Human Factors in Computing Systems

May 7 - 12, 2011

BC, Vancouver, Canada

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CHI '11 Paper Acceptance Rate 410 of 1,532 submissions, 27%;

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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Cited By

Kumar PZimmer MVitak J(2024)A Roadmap for Applying the Contextual Integrity Framework in Qualitative Privacy ResearchProceedings of the ACM on Human-Computer Interaction10.1145/36537108:CSCW1(1-29)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3653710
Ghosh SMandi SSen SMitra BDe P(2024)Towards Estimating Missing Emotion Self-reports Leveraging User Similarity: A Multi-task Learning ApproachProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642833(1-19)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642833
Lee HJung YLaw HBae SLee U(2024)PriviAware: Exploring Data Visualization and Dynamic Privacy Control Support for Data Collection in Mobile Sensing ResearchProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642815(1-17)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642815
Alnuaimi RAlawida M(2024)A Multi-Layered Strategy for Addressing Privacy and Data Security Issues in Metaverse2024 2nd International Conference on Intelligent Metaverse Technologies & Applications (iMETA)10.1109/iMETA62882.2024.10807933(113-118)Online publication date: 26-Nov-2024
https://doi.org/10.1109/iMETA62882.2024.10807933
Kim HKim GLee TKim KLee D(2024)A dataset of ambient sensors in a meeting room for activity recognitionScientific Data10.1038/s41597-024-03344-711:1Online publication date: 21-May-2024
https://doi.org/10.1038/s41597-024-03344-7
Almeida AMateus-Coelho NLopes N(2024)Paranoid Operating System: Wearable TrackersProcedia Computer Science10.1016/j.procs.2024.06.433239(2395-2404)Online publication date: 2024
https://doi.org/10.1016/j.procs.2024.06.433
Gupta AShanker U(2024)Location Privacy Preservation for Location Based Service Applications: Taxonomies, Issues and Future Research DirectionsWireless Personal Communications10.1007/s11277-024-10977-9134:3(1617-1639)Online publication date: 6-Apr-2024
https://doi.org/10.1007/s11277-024-10977-9
Xing YLu HZhao LCao S(2024)Privacy and Security Issues in Mobile Medical Information Systems MMISMobile Networks and Applications10.1007/s11036-024-02299-829:3(762-773)Online publication date: 9-Sep-2024
https://doi.org/10.1007/s11036-024-02299-8
Fischer-Hübner SKaregar FFischer-Hübner SKaregar F(2024)Overview of Usable Privacy Research: Major Themes and Research DirectionsThe Curious Case of Usable Privacy10.1007/978-3-031-54158-2_3(43-102)Online publication date: 20-Mar-2024
https://doi.org/10.1007/978-3-031-54158-2_3
Vlachos VStamatiou YNikoletseas S(2023)The Privacy Flag Observatory: A Crowdsourcing Tool for Real Time Privacy Threats EvaluationJournal of Cybersecurity and Privacy10.3390/jcp30100033:1(26-43)Online publication date: 29-Jan-2023
https://doi.org/10.3390/jcp3010003
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