Privacy implications of accelerometer data: a review of possible inferences <s> INTRODUCTION <s> More and more personal devices such as mobile phones and multimedia players use embedded sensing. This means that people are wearing and carrying devices capable of sensing details about them such as their activity, location, and environment. In this paper, we explore privacy concerns about such personal sensing through interviews with 24 participants who took part in a three month study that used personal sensing to detect their physical activities. Our results show that concerns often depended on what was being recorded, the context in which participants worked and lived and thus would be sensed, and the value they perceived would be provided. We suggest ways in which personal sensing can be made more privacy-sensitive to address these concerns. <s> BIB001 </s> Privacy implications of accelerometer data: a review of possible inferences <s> INTRODUCTION <s> Indoor tracking systems will be an essential part of the home of the future, enabling location-aware and individually-tailored services. However, today there are no tracking solutions that are practical for "every day" use in the home. In this paper, we introduce the Doorjamb tracking system that uses ultrasonic range finders mounted above each doorway, pointed downward to sense people as they walk through the doorway. The system differentiates people by measuring their heights, infers their walking direction using signal processing, and identifies their room locations based on the sequence of doorways through which they pass. Doorjamb provides room-level tracking without requiring any user participation, wearable devices, privacy-intrusive sensors, or high-cost sensors. We create a proof-of-concept implementation and empirically evaluate Doorjamb with experiments that include over 3000 manually-recorded doorway crossings. Results indicate that the system can perform room-level tracking with 90% accuracy on average. <s> BIB002 </s> Privacy implications of accelerometer data: a review of possible inferences <s> INTRODUCTION <s> We demonstrate how the multitude of sensors on a smartphone can be used to construct a reliable hardware fingerprint of the phone. Such a fingerprint can be used to de-anonymize mobile devices as they connect to web sites, and as a second factor in identifying legitimate users to a remote server. We present two implementations: one based on analyzing the frequency response of the speakerphone-microphone system, and another based on analyzing device-specific accelerometer calibration errors. Our accelerometer-based fingerprint is especially interesting because the accelerometer is accessible via JavaScript running in a mobile web browser without requesting any permissions or notifying the user. We present the results of the most extensive sensor fingerprinting experiment done to date, which measured sensor properties from over 10,000 mobile devices. We show that the entropy from sensor fingerprinting is sufficient to uniquely identify a device among thousands of devices, with low probability of collision. <s> BIB003 </s> Privacy implications of accelerometer data: a review of possible inferences <s> INTRODUCTION <s> While mobile sensing applications are booming, the sensor management mechanisms in current smartphone operating systems are left behind -- they are incomprehensive and coarse-grained, exposing a huge attack surface for malicious or aggressive third party apps to steal user's private information through mobile sensors. In this paper, we propose a privacy-aware sensor management framework, called SemaDroid, which extends the existing sensor management framework on Android to provide comprehensive and fine-grained access control over onboard sensors. SemaDroid allows the user to monitor the sensor usage of installed apps, and to control the disclosure of sensing information while not affecting the app's usability. Furthermore, SemaDroid supports context-aware and quality-of-sensing based access control policies. The enforcement and update of the policies are in real-time. Detailed design and implementation of SemaDroid on Android are presented to show that SemaDroid works compatible with the existing Android security framework. Demonstrations are also given to show the capability of SemaDroid on sensor management and on defeating emerging sensor-based attacks. Finally, we show the high efficiency and security of SemaDroid. <s> BIB004 </s> Privacy implications of accelerometer data: a review of possible inferences <s> INTRODUCTION <s> Actitracker is a smartphone-based activity-monitoring service to help people ensure they receive sufficient activity to maintain proper health. This free service allowed people to set personal activity goals and monitor their progress toward these goals. Actitracker uses machine learning methods to recognize a user's activities. It initially employs a "universal" model generated from labeled activity data from a panel of users, but will automatically shift to a much more accurate personalized model once a user completes a simple training phase. Detailed activity reports and statistics are maintained and provided to the user. Actitracker is a research-based system that began in 2011, before fitness trackers like Fitbit were popular, and was deployed for public use from 2012 until 2015, during which period it had 1,000 registered users. This paper describes the Actitracker system, its use of machine learning, and user experiences. While activity recognition has now entered the mainstream, this paper provides insights into applied activity recognition, something that commercial companies rarely share. <s> BIB005 </s> Privacy implications of accelerometer data: a review of possible inferences <s> INTRODUCTION <s> Privacy inference attacks based on sensor data is an emerging and severe threat on smart devices, in which malicious applications leverage data from innocuous sensors to infer sensitive information of user, e.g., utilizing accelerometers to infer user’s keystroke. In this paper, we present Sensor Guardian, a privacy protection system that mitigates this threat on Android by hooking and controlling applications’ access to sensors. Sensor Guardian inserts hooks into applications by statically instrumenting their APK (short for Android Package Kit) files and enforces control policies in these hooks at runtime. Our evaluation shows that Sensor Guardian can effectively and efficiently mitigate the privacy inference threat on Android sensors, with negligible overhead during both static instrumentation and runtime control. <s> BIB006
An accelerometer is an instrument for measuring acceleration forces caused by the movements and vibrations of an object, or by gravity. Today, all sorts of mobile devices, including smartphones, tablet PCs, smartwatches, digital cameras, wearable fitness trackers, game controllers, and virtual reality headsets, are equipped with built-in microelectromechanical accelerometers  . Studies even suggest that accelerometers are the most widely used sensor in wearable devices  and also the sensor that is most frequently accessed by mobile apps BIB006 . Among other common applications, acceleration signals are used for image stabilization in cameras, for measuring the orientation of a device relative to Earth's gravitational pull (e.g. to enable automatic display rotation between landscape and portrait mode), and for detecting user actions, such as moving or shaking a device. While some sensors, such as microphones, cameras and GPS, are widely perceived as privacy-sensitive BIB002 BIB001 and require explicit user permission to be activated in current mobile operating systems BIB006 , accelerometers are less well-understood in terms of their privacy implications, and also much less protected BIB003 BIB004 . Even scholarly literature has largely ignored potential issues in this field, with researchers describing accelerometer data as "not particularly sensitive" BIB005 or even "privacy preserving"  . Experimental studies have shown, however, that sensitive personal data can be inferred from accelerometer readings. This paper presents a non-exhaustive overview of possible inferences, drawing from multiple academic disciplines, including information science, psychology, health science, and computer science. According to our findings, accelerometers in mobile devices may reveal information about a user's activities (section 2.1), location (sect. 2.2), identity (sect. 2.3), device inputs (sect. 2.4), health condition and body features (sect. 2.5), age and gender (sect. 2.6), moods and emotions (sect. 2.7), and personality traits (sect. 2.8).