Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/2906388.2906405acmconferencesArticle/Chapter ViewAbstractPublication PagesmobisysConference Proceedingsconference-collections
research-article
Public Access

What You Mark is What Apps See

Published: 20 June 2016 Publication History

Abstract

Users are increasingly vulnerable to inadvertently leaking sensitive information through cameras. In this paper, we investigate an approach to mitigating the risk of such inadvertent leaks called privacy markers. Privacy markers give users fine-grained control of what visual information an app can access through a device's camera. We present two examples of this approach: PrivateEye, which allows a user to mark regions of a two-dimensional surface as safe to release to an app, and WaveOff, which does the same for three-dimensional objects. We have integrated both systems with Android's camera subsystem. Experiments with our prototype show that a Nexus 5 smartphone can deliver near realtime frame rates while protecting secret information, and a 26-person user study elicited positive feedback on our prototype's speed and ease-of-use.

References

[1]
P. Aditya, R. Sen, S. J. Oh, R. Benenson, B. Bhattacharjee, P. Druschel, T. T. Wu, M. Fritz, and B. Schiele. I-pic: A platform for privacy-compliant image capture. MobiSys, 2016.
[2]
J. Canny. A computational approach to edge detection. TMAPI, 1986.
[3]
S. Chakraborty, C. Shen, K. R. Raghavan, Y. Shoukry, M. Millar, and M. Srivastava. ipshield: A framework for enforcing context-aware privacy. In NSDI, 2014.
[4]
J. Chaudhari, S. Cheung, and M. Venkatesh. Privacy protection for life-log video. In SAFE, 2007.
[5]
D. H. Douglas and T. K. Peucker. Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Cartographica: The International Journal for Geographic Information and Geovisualization, 1973.
[6]
M. Enev, J. Jung, L. Bo, X. Ren, and T. Kohno. Sensorsift: Balancing sensor data privacy and utility in automated face understanding. ACSAC, 2012.
[7]
J. Howell and S. Schechter. What you see is what they get: Protecting users from unwanted use of microphones, cameras, and other sensors. In Web 2.0 Security and Privacy. IEEE, May 2010.
[8]
Q. T. Inc. Trepn Power Profiler. https://developer.qualcomm.com/software/trepn-power-profiler. {Online; accessed 7-Dec-2015}.
[9]
S. Jana, D. Molnar, A. Moshchuk, A. Dunn, B. Livshits, H. J. Wang, and E. Ofek. Enabling Fine-Grained Permissions for Augmented Reality Applications With Recognizers. In USENIX Security, 2013.
[10]
S. Jana, A. Narayanan, and V. Shmatikov. A Scanner Darkly: Protecting User Privacy from Perceptual Applications. In S & P, 2013.
[11]
M. Korayem, R. Templeman, D. Chen, D. J. Crandall, and A. Kapadia. Screenavoider: Protecting computer screens from ubiquitous cameras. CoRR, 2014.
[12]
S. Leutenegger, M. Chli, and R. Siegwart. Brisk: Binary robust invariant scalable keypoints. In ICCV, 2011.
[13]
G. Nebehay and R. Pflugfelder. Clustering of Static-Adaptive correspondences for deformable object tracking. In CVPR, 2015.
[14]
N. Raval, A. Srivastava, K. Lebeck, L. Cox, and A. Machanavajjhala. Markit: Privacy markers for protecting visual secrets. UbiComp '14 Adjunct, 2014.
[15]
F. Roesner, D. Molnar, A. Moshchuk, T. Kohno, and H. J. Wang. World-driven access control for continuous sensing. Technical Report MSR-TR-2014-67, 2014.
[16]
J. Schiff, M. Meingast, D. Mulligan, S. Sastry, and K. Goldberg. Respectful cameras: detecting visual markers in real-time to address privacy concerns. In IROS, 2007.
[17]
J. Shi and C. Tomasi. Good features to track. 1994.
[18]
S. Suzuki and K. Abe. Topological structural analysis of digitized binary images by border following. Computer Vision, Graphics, and Image Processing, 1985.
[19]
R. Templeman, M. Korayem, D. Crandall, and A. Kapadia. PlaceAvoider: Steering first-person cameras away from sensitive spaces. In NDSS, 2014.
[20]
J. Vilk, D. Molnar, E. Ofek, C. Rossbach, B. Livshits, A. Moshchuk, H. J. Wang, and R. Gal. Surroundweb: Mitigating privacy concerns in a 3d web browser. In IEEE Symposium on Security and Privacy, May 2015.
[21]
J. yves Bouguet. Pyramidal implementation of the lucas kanade feature tracker. Intel Corporation, Microprocessor Research Labs, 2000.

Cited By

View all
  • (2024)Exploring Computing Paradigms for Electric Vehicles: From Cloud to Edge Intelligence, Challenges and Future DirectionsWorld Electric Vehicle Journal10.3390/wevj1502003915:2(39)Online publication date: 26-Jan-2024
  • (2024)AragornProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36314067:4(1-31)Online publication date: 12-Jan-2024
  • (2023)ErebusProceedings of the 32nd USENIX Conference on Security Symposium10.5555/3620237.3620290(929-946)Online publication date: 9-Aug-2023
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
MobiSys '16: Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services
June 2016
440 pages
ISBN:9781450342698
DOI:10.1145/2906388
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]

Sponsors

In-Cooperation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 June 2016

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. computer vision
  2. mobile computing
  3. privacy

Qualifiers

  • Research-article

Funding Sources

  • DARPA
  • NSF

Conference

MobiSys'16
Sponsor:

Acceptance Rates

MobiSys '16 Paper Acceptance Rate 31 of 197 submissions, 16%;
Overall Acceptance Rate 274 of 1,679 submissions, 16%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)150
  • Downloads (Last 6 weeks)25
Reflects downloads up to 29 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2024)Exploring Computing Paradigms for Electric Vehicles: From Cloud to Edge Intelligence, Challenges and Future DirectionsWorld Electric Vehicle Journal10.3390/wevj1502003915:2(39)Online publication date: 26-Jan-2024
  • (2024)AragornProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36314067:4(1-31)Online publication date: 12-Jan-2024
  • (2023)ErebusProceedings of the 32nd USENIX Conference on Security Symposium10.5555/3620237.3620290(929-946)Online publication date: 9-Aug-2023
  • (2023)LocInProceedings of the 32nd USENIX Conference on Security Symposium10.5555/3620237.3620287(877-894)Online publication date: 9-Aug-2023
  • (2023)Saliency-Aware Privacy Protection in Augmented Reality SystemsProceedings of the First Workshop on Metaverse Systems and Applications10.1145/3597063.3597358(1-6)Online publication date: 18-Jun-2023
  • (2023)Virtual Curtain: A Communicative Fine-grained Privacy Control Framework for Augmented Reality2023 International Conference on Computing, Networking and Communications (ICNC)10.1109/ICNC57223.2023.10074372(188-194)Online publication date: 20-Feb-2023
  • (2023)Privacy and Security Issues and Solutions for Mixed Reality ApplicationsSpringer Handbook of Augmented Reality10.1007/978-3-030-67822-7_7(157-183)Online publication date: 1-Jan-2023
  • (2022)Understanding Emerging Obfuscation Technologies in Visual Description Services for Blind and Low Vision PeopleProceedings of the ACM on Human-Computer Interaction10.1145/35555706:CSCW2(1-33)Online publication date: 11-Nov-2022
  • (2022)Hidden in Plain Sight: Exploring Privacy Risks of Mobile Augmented Reality ApplicationsACM Transactions on Privacy and Security10.1145/352402025:4(1-35)Online publication date: 9-Jul-2022
  • (2022)Boosting remote multi-user AR privacy through a magic ropeProceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services10.1145/3498361.3538795(583-584)Online publication date: 27-Jun-2022
  • Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

EPUB

View this article in ePub.

ePub

Login options

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media