When downloading a new app, which criterion should you look for to keep your mobile device secure?

ABSTRACT

When downloading a new app, which criterion should you look for to keep your mobile device secure?

People use their mobile devices anywhere and anytime to run various apps, and the information shown on their device screens can be seen by nearby (unauthorized) parties, called shoulder surfers. To mitigate this privacy threat, we have developed HideScreen by utilizing the human vision and optical system properties to hide the users' on-screen information from the shoulder surfers.

Specifically, HideScreen discretizes the device screen into grid patterns to neutralize the low-frequency components so that the on-screen information will "blend into'' the background when viewed from the outside of the designed range. We have developed and evaluated several ways of hiding both on-screen texts and images from shoulder surfers. Our extensive experimental evaluation of HideScreen demonstrates its high protection rates (>96% for texts and >99% for images) while providing good user experience.

References

  1. Malin Eiband, Mohamed Khamis, Emanuel von Zezschwitz, Heinrich Hussmann, and Florian Alt. Understanding Shoulder Surfing in the Wild. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems - CHI '17, pages 4254--4265, New York, New York, USA, 2017. ACM Press. Google ScholarDigital Library
  2. Adam J. Aviv, John T. Davin, Flynn Wolf, and Ravi Kuber. Towards Baselines for Shoulder Surfing on Mobile Authentication. In Proceedings of the 33rd Annual Computer Security Applications Conference on - ACSAC 2017, pages 486--498, New York, New York, USA, 2017. ACM Press. Google ScholarDigital Library
  3. Polotiko. Aguirre furious at photo leak of private text message. http://politics.com.ph/aguirre-furious-photo-leak-private-text-message/, 2017. Accessed: 2018-01-31.Google Scholar
  4. HP. HP Introduces World's Only Notebooks with Integrated Privacy Screens. https://press.ext.hp.com/us/en/press-releases/2016/hp-introduces-worlds-only-notebooks-with-integrated-privacy-scre.html, 2016. Accessed: 2018-07--28.Google Scholar
  5. Hee Jung Ryu and Florian Schroff. Electronic Screen Protector with Efficient and Robust Mobile Vision Demo Video. https://nips.cc/Conferences/2017/Schedule'showEvent=9757, 2017. Accessed: 2018-02-01.Google Scholar
  6. BlackBerry Limited. BlackBerry Privacy Shade - Android Apps on Google Play. https://goo.gl/MFGeAX, 2016. Accessed: 2018-02-01.Google Scholar
  7. Athanasios Papadopoulos, Toan Nguyen, Emre Durmus, and Nasir Memon. IllusionPIN: Shoulder-Surfing Resistant Authentication Using Hybrid Images. IEEE Transactions on Information Forensics and Security, 12(12):2875--2889, Dec 2017.Google ScholarCross Ref
  8. Aude Oliva, Antonio Torralba, and Philippe G. Schyns. Hybrid images. In ACM SIGGRAPH 2006 Papers on - SIGGRAPH '06, volume 25, pages 527--532, New York, New York, USA, 2006. ACM Press. Google ScholarDigital Library
  9. NASA. Anthropometry and Biomechanics. https://msis.jsc.nasa.gov/sections/section03.htm, 2000. Accessed: 2017-11-27.Google Scholar
  10. Michitaka Yoshimura, Momoko Kitazawa, Yasuhiro Maeda, Masaru Mimura, Kazuo Tsubota, and Taishiro Kishimoto. Smartphone viewing distance and sleep: an experimental study utilizing motion capture technology. Nature and science of sleep, 9:59--65, 2017.Google Scholar
  11. Wikipedia. Airline seat, 2017. Accessed: 2017--12--29.Google Scholar
  12. Devraj Singh. Fundamentals of optics. Prentice-Hall Of India, 2015.Google Scholar
  13. J. Mannos and D. Sakrison. The effects of a visual fidelity criterion of the encoding of images. IEEE Transactions on Information Theory, 20(4):525--536, Jul 1974. Google ScholarDigital Library
  14. Latanya Sweeney. k-Anonymity: A Model For Protecting Privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(05):557--570, Oct 2002. Google ScholarDigital Library
  15. Jakub Dostal, Ola Kristensson, and Aaron Quigley. Estimating and using absolute and relative viewing distance in interactive systems. Pervasive and Mobile Computing, 10:173--186, 2014. Google ScholarDigital Library
  16. Tech Armor. Tech Armor Website. https://techarmor.com/, 2018.Google Scholar
  17. Google. Vision API -- Image Content Analysis. https://cloud.google.com/vision/, 2018. Accessed: 2018-01--29.Google Scholar
  18. Ze-Nian Li, Mark S. Drew, and Jiangchuan Liu. Introduction to Multimedia. Springer, 2014.Google ScholarCross Ref
  19. Susanne Trauzettel-Klosinski and Klaus Dietz. Standardized Assessment of Reading Performance: The New International Reading Speed Texts IReST. Investigative Opthalmology & Visual Science, 53(9):5452, Aug 2012.Google ScholarCross Ref
  20. David Beymer, Daniel Russell, and Peter Orton. An Eye Tracking Study of How Font Size and Type Influence Online Reading. In Proceedings of the 22nd British HCI Group Annual Conference on People and Computers: Culture, Creativity, Interaction - Volume 2, pages 15--18. British Computer Society, 2008. Google ScholarDigital Library
  21. Iain Darroch, Joy Goodman, Stephen Brewster, and Phil Gray. The Effect of Age and Font Size on Reading Text on Handheld Computers. pages 253--266. Springer, Berlin, Heidelberg, 2005. Google ScholarDigital Library
  22. Mark C Russell and Barbara S Chaparro. Exploring Effects Of Speed And Font Size With RSVP. In Proceedings of the Human Factors And Ergonimics Society 45th Annual Meeting, 2001.Google Scholar
  23. Facebook. Making Visual Messaging Even Better -- Introducing High Resolution Photos in Messenger. https://newsroom.fb.com/news/2017/11/making-visual-messaging-even-better-introducing-high-resolution-photos-in-messenger/, 2017. Accessed: 2018-01--29.Google Scholar
  24. Facebook. We now have over 1.2 billion people actively using Messenger every month. https://goo.gl/diJ4t4, 2017. Accessed: 2018-01-29.Google Scholar
  25. John Brooke. SUS - A quick and dirty usability scale Usability and context. Usability evaluation in industry, 1986.Google Scholar
  26. Aaron Bangor, Philip Kortum, and James Miller. Determining What Individual SUS Scores Mean: Adding an Adjective Rating Scale. Journal of Usability Studies, 4(3):114--123, 2009. Google ScholarDigital Library
  27. Jeff Sauro. MeasuringU: Measuring Usability with the System Usability Scale (SUS), 2011.Google Scholar
  28. W; Eekelen, J; Van Den Elst, J V Khan, Wouter Van Eekelen, John Van Den Elst, and Vassilis-Javed Khan. Dynamic layering graphical elements for graphical password schemes. Proceedings of the Chi Sparks 2014 Conference, pages 65--73, 2014.Google Scholar
  29. Haichang Gao, Zhongjie Ren, Xiuling Chang, Xiyang Liu, and Uwe Aickelin. A New Graphical Password Scheme Resistant to Shoulder-Surfing. In 2010 International Conference on Cyberworlds, pages 194--199. IEEE, Oct 2010. Google ScholarDigital Library
  30. Jan Gugenheimer, Alexander De Luca, Hayato Hess, Stefan Karg, Dennis Wolf, and Enrico Rukzio. ColorSnakes: Using Colored Decoys to Secure Authentication in Sensitive Contexts. In Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services - MobileHCI '15, pages 274--283, New York, New York, USA, 2015. ACM Press. Google ScholarDigital Library
  31. Nur Haryani Zakaria, David Griffiths, Sacha Brostoff, and Jeff Yan. Shoulder surfing defense for recall-based graphical passwords. In Proceedings of the Seventh Symposium on Usable Privacy and Security - SOUPS '11, New York, New York, USA, 2011. ACM Press. Google ScholarDigital Library
  32. Alexander De Luca, Katja Hertzschuch, and Heinrich Hussmann. ColorPIN -- Securing PIN Entry through Indirect Input. In Proceedings of the 28th international conference on Human factors in computing systems - CHI '10, pages 1103--1106, New York, New York, USA, 2010. ACM Press. Google ScholarDigital Library
  33. Volker Roth, Kai Richter, and Rene Freidinger. A PIN-entry method resilient against shoulder surfing. In Proceedings of the 11th ACM conference on Computer and communications security - CCS '04, pages 236--245, New York, New York, USA, 2004. ACM Press. Google ScholarDigital Library
  34. Susan Wiedenbeck, Jim Waters, Leonardo Sobrado, and Jean-Camille Birget. Design and evaluation of a shoulder-surfing resistant graphical password scheme. In Proceedings of the working conference on Advanced visual interfaces - AVI '06, pages 177--184, New York, New York, USA, 2006. ACM Press. Google ScholarDigital Library
  35. Abdullah Ali, Ravi Kuber, and Adam J Aviv. Developing and evaluating a gestural and tactile mobile interface to support user authentication. In IConference 2016 Proceedings, 2016.Google ScholarCross Ref
  36. Andrea Bianchi, Ian Oakley, Vassilis Kostakos, and Dong Soo Kwon. The phone lock: audio and haptic shoulder-surfing resistant PIN entry methods for mobile devices. In Proceedings of the fifth international conference on Tangible, embedded, and embodied interaction - TEI '11, pages 197--200, New York, New York, USA, 2011. ACM Press. Google ScholarDigital Library
  37. Alexander De Luca, Martin Denzel, and Heinrich Hussmann. Look into my eyes!: can you guess my password? In Proceedings of the 5th Symposium on Usable Privacy and Security - SOUPS '09, New York, New York, USA, 2009. ACM Press. Google ScholarDigital Library
  38. Alexander De Luca, Alina Hang, Frederik Brudy, Christian Lindner, and Heinrich Hussmann. Touch me once and I know it's you!: implicit authentication based on touch screen patterns. In Proceedings of the 2012 ACM annual conference on Human Factors in Computing Systems - CHI '12, pages 987--996, New York, New York, USA, 2012. ACM Press. Google ScholarDigital Library
  39. Alexander De Luca, Marian Harbach, Emanuel von Zezschwitz, Max-Emanuel Maurer, Bernhard Ewald Slawik, Heinrich Hussmann, and Matthew Smith. Now you see me, now you don't: protecting smartphone authentication from shoulder surfers. In Proceedings of the 32nd annual ACM conference on Human factors in computing systems - CHI '14, pages 2937--2946, New York, New York, USA, 2014. ACM Press. Google ScholarDigital Library
  40. Alexander De Luca, Emanuel Von Zezschwitz, and Heinrich Hußmann. VibraPass - Secure Authentication Based on Shared Lies. In CHI, pages 913--916, 2009. Google ScholarDigital Library
  41. Alain Forget, Sonia Chiasson, and Robert Biddle. Shoulder-surfing resistance with eye-gaze entry in cued-recall graphical passwords. In Proceedings of the 28th international conference on Human factors in computing systems - CHI '10, pages 1107--1110, New York, New York, USA, 2010. ACM Press. Google ScholarDigital Library
  42. Mohamed Khamis, Florian Alt, Mariam Hassib, Emanuel von Zezschwitz, Regina Hasholzner, and Andreas Bulling. GazeTouchPass. In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems - CHI EA '16, pages 2156--2164, New York, New York, USA, 2016. ACM Press. Google ScholarDigital Library
  43. Behzad Malek, Mauricio Orozco, and Abdulmotaleb El Saddik. Novel Shoulder-Surfing Resistant Haptic-based Graphical Password. In Proc. EuroHaptics, Vol. 6, 2006.Google Scholar
  44. Toan Van Nguyen, Napa Sae-Bae, and Nasir Memon. DRAW-A-PIN: Authentication using finger-drawn PIN on touch devices. Computers & Security, 66:115--128, May 2017. Google ScholarDigital Library
  45. Christian Winkler, Jan Gugenheimer, Alexander De Luca, Gabriel Haas, Philipp Speidel, David Dobbelstein, and Enrico Rukzio. Glass Unlock: Enhancing Security of Smartphone Unlocking through Leveraging a Private Near-eye Display. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems - CHI '15, pages 1407--1410, New York, New York, USA, 2015. ACM Press. Google ScholarDigital Library
  46. Malin Eiband, Emanuel von Zezschwitz, Daniel Buschek, and Heinrich Hußmann. My Scrawl Hides It All. In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems - CHI EA '16, pages 2041--2048, New York, New York, USA, 2016. ACM Press. Google ScholarDigital Library
  47. Emanuel von Zezschwitz, Sigrid Ebbinghaus, Heinrich Hussmann, and Alexander De Luca. You Can't Watch This!: Privacy-Respectful Photo Browsing on Smartphones. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems - CHI '16, pages 4320--4324, New York, New York, USA, 2016. ACM Press. Google ScholarDigital Library
  48. Mohammed Eunus Ali, Tanzima Hashem, Anika Anwar, Lars Kulik, Ishrat Ahmed, and Egemen Tanin. Protecting Mobile Users from Visual Privacy Attacks. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing Adjunct Publication - UbiComp '14 Adjunct, pages 1--4, New York, New York, USA, 2014. ACM Press. Google ScholarDigital Library
  49. Frederik Brudy, David Ledo, Saul Greenberg, and Andreas Butz. Is Anyone Looking? Mitigating Shoulder Surfing on Public Displays through Awareness and Protection. In Proceedings of The International Symposium on Pervasive Displays - PerDis '14, pages 1--6, New York, New York, USA, 2014. ACM Press. Google ScholarDigital Library
  50. Shiguo Lian, Wei Hu, Xingguang Song, and Zhaoxiang Liu. Smart privacy-preserving screen based on multiple sensor fusion. IEEE Transactions on Consumer Electronics, 59(1):136--143, Feb 2013.Google ScholarCross Ref
  51. Peter Tarasewich, Richard Conlan, and Jun Gong. Protecting Private Data in Public. In Extended Abstracts Proceedings of the 2006 Conference on Human Factors in Computing Systems, 2006. Google ScholarDigital Library

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  1. Keep Others from Peeking at Your Mobile Device Screen!

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      When downloading a new app, which criterion should you look for to keep your mobile device secure?

      MobiCom '19: The 25th Annual International Conference on Mobile Computing and Networking

      August 2019

      1017 pages

      Copyright © 2019 ACM

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      • Online: 5 August 2019

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