Journal:What is this sensor and does this app need access to it?

From LIMSWiki
Revision as of 00:31, 26 November 2019 by Shawndouglas (talk | contribs) (Saving and adding more.)
Jump to navigationJump to search
Full article title What is this sensor and does this app need access to it?
Journal Informatics
Author(s) Mehrnezhad, Maaryam; Toreini, Ehsan
Author affiliation(s) Newcastle University
Primary contact Email: maryam dot mehrnezhad at ncl dot ac dot uk
Year published 2019
Volume and issue 6(1)
Page(s) 7
DOI 10.3390/informatics6010007
ISSN 2227-9709
Distribution license Creative Commons Attribution 4.0 International
Website https://www.mdpi.com/2227-9709/6/1/7/htm
Download https://www.mdpi.com/2227-9709/6/1/7/pdf (PDF)
Emblem-important-yellow.svg This article should be considered a work in progress and incomplete. Consider this article incomplete until this notice is removed.

Abstract

Mobile sensors have already proven to be helpful in different aspects of people’s everyday lives such as fitness, gaming, navigation, etc. However, illegitimate access to these sensors results in a malicious program running with an exploit path. While users are benefiting from richer and more personalized apps, the growing number of sensors introduces new security and privacy risks to end-users and makes the task of sensor management more complex. In this paper, we first discuss the issues around the security and privacy of mobile sensors. We investigate the available sensors on mainstream mobile devices and study the permission policies that Android, iOS and mobile web browsers offer for them. Second, we reflect on the results of two workshops that we organized on mobile sensor security. In these workshops, the participants were introduced to mobile sensors by working with sensor-enabled apps. We evaluated the risk levels perceived by the participants for these sensors after they understood the functionalities of these sensors. The results showed that knowing sensors by working with sensor-enabled apps would not immediately improve the users’ security inference of the actual risks of these sensors. However, other factors such as the prior general knowledge about these sensors and their risks had a strong impact on the users’ perception. We also taught the participants about the ways that they could audit their apps and their permissions. Our findings showed that when mobile users were provided with reasonable choices and intuitive teaching, they could easily self-direct themselves to improve their security and privacy. Finally, we provide recommendations for educators, app developers, and mobile users to contribute toward awareness and education on this topic.

Keywords: mobile sensors, IoT sensors, sensor security, security education, app permission, mobile security awareness, user privacy, user security, sensor attacks

Introduction

According to The Economist[1], smartphones have become the fastest-selling gadgets in history, outselling personal computers (PCs) four to one. Today, about half the adult population owns a smartphone; by 2020, 80% will. Mobile and smart device vendors are increasingly augmenting their products with various types of sensors such as the Hall effect sensor, accelerometer, NFC (near-field communication) sensor, heart rate sensor, and iris scanner, which are connected to each other through the internet of things (IoT). We have observed that approximately 10 new sensors have been augmented or became popular in mainstream mobile devices in less than two years, bringing the number of mobile sensors to more than 30 sensors. Examples include FaceID, Active edge, depth cameras (using infrared), thermal cameras, air sensors, laser sensors, haptic sensors, iris scanners, heart rate sensors, and body sensors.

Sensors are added to mobile and other devices to make them smart: to sense the surrounding environment and infer aspects of the context of use, and thus to facilitate more meaningful interactions with the user. Many of these sensors are used in popular mobile apps such as fitness trackers and games. Mobile sensors have also been proposed for security purposes, e.g., authentication[2][3], authorization[4], device pairing[5], and secure contactless payment.[6] However, malicious access to sensor streams results in an installed app running in the background with an exploit path. Researchers have shown that user PINs and passwords can be disclosed through sensors such as the camera and microphone[7], the ambient light sensor[8], and the gyroscope.[9] Sensors such as NFC can also be misused to attack financial payments.[10]

In our previous research[11][12][13][14], we have shown that the sensor management problem is spreading from apps to browsers. We proposed and implemented the first JavaScript-based side channel attack revealing a wide range of sensitive information about users such as phone calls’ timing, physical activities (sitting, walking, running, etc.), touch actions (click, hold, scroll, and zoom) and PINs on mobile phones. In this attack, the JavaScript code embedded in the attack web page listens to the motion and orientation sensor streams without needing any permission from the user. By analyzing these streams via machine learning algorithms, this attack infers the user’s touch actions and PINs with an accuracy of over 70% on the first try. The above research attracted considerable international media coverage, including by the Guardian[15] and the BBC[16], which reassures the importance of the topic. We disclosed the identified vulnerability described in the above to the industry. While working with World Wide Web Consortium (W3C) and browser vendors (Google Chromium, Mozilla Firefox, Apple, etc.) to fix the problem, we came to appreciate the complexity of the sensor management problem in practice and the challenge of balancing security, usability, and functionality.

Through a series of user studies over the years[13][14], we concluded that mobile users are not generally familiar with most sensors. In addition, we observed that there is a significant disparity between the actual and perceived risk levels of sensors. In another work[17], the same conclusion was made by Crager et. al. for motion sensors. We discussed how this observation, along with other factors, renders many academic and industry solutions ineffective at managing mobile sensors.[14] Given that sensors are going beyond mobile devices, e.g., in a variety of IoT devices in smart homes and cities, the sensor security problem has already attracted more attention not only from researchers, but also from hackers. In view of all this, we believe that there is much room for more focus on people’s awareness and education about the privacy and security issues of sensor technology.

Previous research[14][17] has focused on individual user studies to study human aspects of sensor security. In this paper, we present the results of a more advanced teaching method—working with sensor-enabled apps—on the risk level that users associate with the PIN discovery scenario for all sensors. We reflect the results of two interactive workshops that we organized on mobile sensor security. These workshops covered the following: an introduction of mobile sensors and their applications, working with sensor-enabled mobile apps, an introduction of the security and privacy issues of mobile sensors, and an overview of how to manage the app permissions on different mobile platforms.

In these workshops, the participants were sitting in groups and introduced to mobile sensors by working with sensor-enabled apps. Throughout the workshops, we asked the participants to fill in a few forms in order to evaluate the general knowledge they had about mobile sensors, as well as their perceived risk levels for these sensors after they understood their functionalities. After analyzing these self-declared forms, we also measured the correlation between the knowledge and perceived risk level for mobile sensors. The results showed that knowing sensors by working with sensor-enabled apps would not immediately improve the users’ security inference of the actual risks of these sensors. However, other factors such as the prior general knowledge about these sensors and their risks have a strong impact on the users’ perception. We also taught the participants about the ways that they could audit their apps and their permissions, including per app vs. per permission. Our participants found both models useful in different ways. Our findings show that when mobile users are provided with reasonable choices and intuitive teaching, they can easily self-direct themselves to improve their security and privacy.

In the next section, we list the available sensors on mobile devices and categorize them, and then we present the current permission policies for these sensors on Android, iOS, and mobile web browsers. In the subsequent section, we present the structure of these workshops in full detail. Afterwards, we include our analysis on the general knowledge and perceived risk levels that our participants had for sensors and their correlation, followed by our observations of the apps’ and permissions’ review activities in the workshops. We then go on to present a list of our recommendations to different stakeholders. Finally, in the final two sections, we include limitations, future work, and the conclusion.

Mobile sensors

As stated, there are more than 30 sensors on mobile devices. Both iOS and Android, as well as mobile web browsers, allow native apps and JavaScript code in web pages to access most of these sensors. Developers can have access to mobile sensors either by (1) writing native code using mobile OS APIs[18][19], (2) recompiling HTML5 code into a native app[20], or (3) using standard APIs provided by the W3C[21], which are accessible through JavaScript code within a mobile browser.

As shown by Taylor and Martinovic[22], the average number of permissions used by Android apps increases over time, in particular for popular apps and free apps. These permissions are requested for having access to the operating system (OS) resources such as contacts and files, as well as sensors such as the GPS and microphone. This has the potential to make apps over-privileged and unnecessarily increase the attack surface.

Mobile sensors' categorization

We first created a list of available sensors on various mobile devices. We prepared this list by inspecting the official websites of mainstream mobile devices such as the iPhone X, Samsung Galaxy S9, Google Pixel 2, as well as the specifications that W3C[23], Android[18], and Apple[19] provide for developers. We proposed categorizing these sensors into four main groups: identity-related (biometric) sensors, communicational sensors, motion sensors, and ambient (environmental) sensors, as presented in Table 1. Note that this list can be even longer if all mobile brands are included. For example, the Cat S61 smart phone has sensors such as a thermal camera, an air sensor (measures the quality of the environmental air), and a laser sensor (to measure distance).

Table 1. Categorization of current mobile sensors
Category Sensors
Identity-related GPS, Camera, Microphone, Fingerprint (TouchID), FaceID, Iris scan, Heart rate (HR)
Identity-related: Biometric Touch screen, Active Edge, Haptic sensor, Body sensors
Communication WiFi, Bluetooth, NFC
Motion Gyroscope, Accelerometer, Rotation, Orientation, Motion, Sensor hub
Ambient Temperature (ambient, device), Humidity, Pressure (barometer), Light, Proximity
Ambient: Environmental Gravity, Magnetic field, Hall effect sensor

In Appendix A (see the supplemental material at the end), we present a brief description of each sensor. With the growing number of sensors on mobile devices, categorizing them into a few groups is much more difficult than before. Some of these sensors can belong to multiple groups. For example, one might argue that GPS belongs to the environmental category; however, since it is associated with people’s identities, we propose to keep it in the identity-related category. Similarly, the sensor hub monitors the device’s movements, which is associated with the user’s activities. Hence, it is difficult to decide to which category (motion or biometric) it belongs.

Sensor management challenges

In Table 2, we present how the Android, iOS, and W3C specs (followed by mobile browsers) treat different sensors in terms of access. We used the Android and Apple developer websites, the W3C specifications, and caniuse.com to build this table.[18][19][23][24] As can be seen, permission policies for having access to different sensors vary across sensors and platforms. We argue that sensing is still unmanaged on existing smartphone platforms. The in-app access to certain sensors including GPS, camera, and microphone requires user permission when installing and running the app. However, as Simon and Anderson have discussed[7], an attacker can easily trick a user into granting permission through social engineering (e.g., presenting it as a free game app). Once the app is installed and the permission approved, usage of the sensor data is not restricted. On the other hand, access to many other sensors such as accelerometers, gyroscopes, and light sensors is unrestricted; any app can have free access to the sensor data without needing any user permission, as these sensors are left unmanaged on mobile operating systems.

Table 2. Current permission policies of sensors on different platforms. ✓: permission required, ✗: permission not required, NA: not supported, and Locked: not open to developers. * NFC should be turned on manually for any program to be able to use it.
Sensor Android iOS W3C/Web Browsers
GPS ✓ ✓ ✓
Camera ✓ ✓ ✓
Microphone ✓ ✓ ✓
Fingerprint/TouchID ✓ ✓ NA
Touch screen ✗ ✗ ✗
FaceID ✓ ✓ NA
Iris scan ✓ ✓ NA
Heart rate ✓ ✓ NA
Body sensors ✓ ✓ NA
Active Edge Locked NA NA
Haptic sensor ✗ ✗ ✗
Wifi ✓ ✓ ✗
Bluetooth ✓ ✓ ✓
NFC ✗* ✗* ✗
Accelerometer ✗ ✗ ✗
Rotation ✗ ✗ ✗
Gyroscope ✗ ✗ ✗
Motion ✗ ✗ ✗
Orientation ✗ ✗ ✗
Sensor hub Locked Locked NA
Proximity ✗ ✗ ✗
Ambient light ✗ ✗ ✗
Ambient pressure/barometer ✗ ✗ NA
Ambient humidity ✗ NA NA
Ambient temperature ✗ NA NA
Device temperature ✗ NA NA
Gravity ✗ ✗ ✗
Magnetic field ✗ ✗ ✗
Hall effect sensor ✗ NA NA

Although the information leakage caused by sensors has been known for years[7][8][9], the problem has remained unsolved in practice. One main reason is the complexity of the problem; keeping the balance between security and usability. Another reason, from the practical perspective, is that all the reported attacks depend on one condition: the user must initiate the downloading and installing of the app. Therefore, users are relied upon to be vigilant and not to install untrusted apps. Furthermore, it is expected that app stores such as the Apple App Store and Google Play will screen the apps and impose severe penalties if the app is found to contain malicious content. However, in the browser-based attack[11][12][13][14], we have demonstrated that these measures are ineffective. Apart from academic efforts, there are industrial solutions (e.g., Navenio) that use some of these sensors such as the accelerometer to track users precisely indoors and outdoors. These products can easily be integrated with illegitimate apps and websites and break user’s privacy and security.

With the growing number of sensors, and more sensitive sensor hardware provisioned with new mobile devices and other IoT devices, the problem of information leakage caused by sensors is becoming more severe. Previous research[14][17] suggests that users are not aware of (i) the data generated by the sensors, (ii) how that data might be used to undermine their security and privacy, and (iii) what precautionary measure they could and should take. Given that, we believe that raising public knowledge about the sensor technology through education is a very timely matter.

Workshop

We ran two rounds of a 90-minute workshop entitled What Your Sensors Say About You, which was hosted by the Thinking Digital conference in November 2016[25] and May 2018 at Newcastle University, U.K. The attendees could find the following description of the workshop on the event page: “Mobile sensors are everywhere. They’re in our smartphones, our tablets and our wearables. They help our devices to detect movement, sense changes in pressure, and notice when other devices are nearby. The data they provide help us to enjoy richer and more personalized apps. But what are the risks to our phones, and the information that lies within them? Discover how these sensors may introduce new security risks to phone users, and make it more complicated to manage them.”

Pedagogical approach

For the purposes of those workshops, our teaching approach, incorporating taught and research dissemination activities, embodied the principles of constructive alignment and constructivist learning theory. In particular, we deliberately introduced a number of periods of reflection throughout the workshop. Attendees were supported in considering various preventative measures in relation to permission-granting in sensor-related apps and extrapolating their future impacts.

A widely adopted theory in the public understanding of scientific research is that of the “deficit model.”[26] The deficit model acknowledges that a lack of available information leads to a lack of popular understanding, which in turn fosters scepticism and hostility. Through our public engagement exercise, and by making available our resources, we seek to equip the public with accessible information, which may inform reasonable precautionary behavior.

We adopt a challenging role, both as researchers active in mobile sensor security and mediators seeking to popularize research findings. This leads to tension between providing layman and specialist explanations, a perennial issue in science communication.[27] As such, we acknowledge the role popularization of science plays in informing future iterations of research.[28][29] Indeed, our observations of participants’ interactions serve to inform future technological interventions to support mobile sensor security.

References

  1. ↑ "Planet of the Phones". The Economist. The Economist Newspaper Limited. 26 February 2015. https://www.economist.com/leaders/2015/02/26/planet-of-the-phones. Retrieved 30 November 2018. 
  2. ↑ De Luca, A.; Hang, A.; Brudy, F. et al. (2012). "Touch me once and i know it's you!: Implicit authentication based on touch screen patterns". Proceedings of the 2012 SIGCHI Conference on Human Factors in Computing Systems: 987–96. doi:10.1145/2207676.2208544. 
  3. ↑ Bo, C.; Zhang, L.; Li, X.-Y. et al. (2013). "SilentSense: Silent user identification via touch and movement behavioral biometrics". Proceedings of the 19th Annual International Conference on Mobile Computing & Networking: 187–90. doi:10.1145/2500423.2504572. 
  4. ↑ Li, H.; Ma, D.; Saxena, N. et al. (2013). "Tap-Wave-Rub: Lightweight malware prevention for smartphones using intuitive human gestures". Proceedings of the Sixth ACM Conference on Security and Privacy in Wireless and Mobile Networks: 25–30. doi:10.1145/2462096.2462101. 
  5. ↑ Mayrhofer, R.; Gellersen, H. (2007). "Shake Well Before Use: Authentication Based on Accelerometer Data". In LaMarca, A.; Langheinrich, M.; Truong, K.N.. Pervasive Computing - Pervasive 2007. pp. 144–61. doi:10.1007/978-3-540-72037-9_9. ISBN 9783540720379. 
  6. ↑ Mehrnezhad, M.; Hao, F.; Shahandashti, S.F. (2015). "Tap-Tap and Pay (TTP): Preventing the Mafia Attack in NFC Payment". In Chen, L.; Matsuo, S.. Security Standardisation Research - SSR 2015. pp. 21–39. doi:10.1007/978-3-319-27152-1_2. ISBN 9783319271521. 
  7. ↑ 7.0 7.1 7.2 Simon, L.; Anderson, R. (2013). "PIN skimmer: Inferring PINs through the camera and microphone". Proceedings of the Third ACM workshop on Security and Privacy in Smartphones & Mobile Devices: 67–78. doi:10.1145/2516760.2516770. 
  8. ↑ 8.0 8.1 Spreitzer, R. (2014). "PIN Skimming: Exploiting the Ambient-Light Sensor in Mobile Devices". Proceedings of the 4th ACM Workshop on Security and Privacy in Smartphones & Mobile Devices: 51–62. doi:10.1145/2666620.2666622. 
  9. ↑ 9.0 9.1 Xu, Z.; Bai, K.; Zhu, S. (2012). "TapLogger: Inferring user inputs on smartphone touchscreens using on-board motion sensors". Proceedings of the Fifth ACM Conference on Security and Privacy in Wireless and Mobile Networks: 113–24. doi:10.1145/2185448.2185465. 
  10. ↑ Mehrnezhad, M.; Ali, M.A.; Hao, F. et al. (2016). "NFC Payment Spy: A Privacy Attack on Contactless Payments". In Chen, L.; Matsuo, S.. Security Standardisation Research - SSR 2016. pp. Article 4. doi:10.1007/978-3-319-49100-4_4. ISBN 9783319491004. 
  11. ↑ 11.0 11.1 Mehrnezhad, M.; Toreini, E.; Shahandashti, S.F. et al. (2016). "TouchSignatures: Identification of user touch actions and PINs based on mobile sensor data via JavaScript". Journal of Information Security and Applications 26: 23–38. doi:10.1016/j.jisa.2015.11.007. 
  12. ↑ 12.0 12.1 Mehrnezhad, M.; Toreini, E.; Shahandashti, S.F. et al. (2015). "TouchSignatures: Identification of user touch actions based on mobile sensors data via JavaScript". Proceedings of the 10th ACM Symposium on Information, Computer and Communications Security: 673. doi:10.1145/2714576.2714650. 
  13. ↑ 13.0 13.1 13.2 Mehrnezhad, M.; Toreini, E.; Shahandashti, S.F. et al. (2016). "Stealing PINs via Mobile Sensors: Actual Risk versus User Perception". Proceedings of the 1st European Workshop on Usable Security, EuroUSEC 2016: 1–14. doi:10.14722/eurousec.2016.23008. https://www.ndss-symposium.org/ndss2016/eurousec-2016-workshop/#session1. 
  14. ↑ 14.0 14.1 14.2 14.3 14.4 14.5 Mehrnezhad, M.; Toreini, E.; Shahandashti, S.F. et al. (2018). "Stealing PINs via mobile sensors: actual risk versus user perception". International Journal of Information Security 17 (3): 291–313. doi:10.1007/s10207-017-0369-x. 
  15. ↑ Hern, A. (11 April 2017). "Tilted device could pinpoint pin number for hackers, study claims". The Guardian. https://www.theguardian.com/technology/2017/apr/11/tilted-device-could-pinpoint-pin-number-for-hackers-study-claims. Retrieved 30 November 2018. 
  16. ↑ "The way people tilt their smartphone 'can give away passwords and pins'". BBC Newsbeat. BBC. 11 April 2017. http://www.bbc.co.uk/newsbeat/article/39565372/the-way-people-tilt-their-smartphone-can-give-away-passwords-and-pins. Retrieved 30 November 2018. 
  17. ↑ 17.0 17.1 17.2 Crager, K.; Maiti, A.; Jadliwala, M. et al. (2017). "Information Leakage through Mobile Motion Sensors: User Awareness and Concerns". Proceedings of the 2nd European Workshop on Usable Security, EuroUSEC 2017: 1–15. doi:10.14722/eurousec.2017.23013. https://www.ndss-symposium.org/eurousec-2017-workshop/. 
  18. ↑ 18.0 18.1 18.2 "Sensors". Android Developer Guides. Google. 2018. https://developer.android.com/guide/topics/sensors/index.html. Retrieved 30 November 2018. 
  19. ↑ 19.0 19.1 19.2 "Core Motion". Apple Developer Documentation. Apple. 2018. https://developer.apple.com/documentation/coremotion. Retrieved 30 November 2018. 
  20. ↑ Jin, X.; Hu, X.; Ying, K. et al. (2014). "Code Injection Attacks on HTML5-based Mobile Apps: Characterization, Detection and Mitigation". Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security: 66–77. doi:10.1145/2660267.2660275. 
  21. ↑ "All Standards and Drafts Tagged as Web API". W3C. https://www.w3.org/TR/?tag=webapi. 
  22. ↑ Taylor, V.F.; Martinovic, I. (6 June 2016). "A Longitudinal Study of App Permission Usage Across the Google Play Store". arXiv. https://arxiv.org/abs/1606.01708v1. Retrieved 30 November 2018. 
  23. ↑ 23.0 23.1 "Device and Sensors Working Group". W3C. Archived from the original on 01 January 2018. https://web.archive.org/web/20180101221340/https://www.w3.org/2009/dap/. 
  24. ↑ "Sensors Overview". Android Developer Guides. Google. 2018. https://developer.android.com/guide/topics/sensors/sensors_overview.html. Retrieved 30 November 2018. 
  25. ↑ "Thinking Digital Women 2016". Mediaworks. Mediaworks UK Limited. 2016. https://www.mediaworks.co.uk/insights/news/mediaworks-blog-post-thinking-digital-women-2016/. 
  26. ↑ Wynne, B. (1992). "Misunderstood misunderstanding: Social identities and public uptake of science". Public Understanding of Science 1 (3): 281-304. doi:10.1088/0963-6625/1/3/004. 
  27. ↑ Sismondo, S. (2010). An Introduction to Science and Technology Studies (2nd ed.). Wiley-Blackwell. pp. 254. ISBN 9781405187657. 
  28. ↑ Hilgartner, S. (1990). "The Dominant View of Popularization: Conceptual Problems, Political Uses". Social Studies of Science 20 (3): 519–39. doi:10.1177/030631290020003006. 
  29. ↑ Bucchi, M. (2014). Science and the Media: Alternative Routes in Scientific Communication. Routledge. pp. 208. ISBN 9780415510516. 

Notes

This presentation is faithful to the original, with only a few minor changes to presentation. Grammar was cleaned up for smoother reading. In some cases important information was missing from the references, and that information was added. A few of the inline URLs were turned into citations for this version. The inline URL to Altmetric and the May 2018 workshop from the original article were removed for this version because they were dead, unarchived URLs. The W3C Device and Sensors Working Group URl also changed; an archived version of the site was used for the citation in this version.

Retrieved from "https://www.limswiki.org/index.php?title=Journal:What_is_this_sensor_and_does_this_app_need_access_to_it%3F&oldid=36925"