Privacy implications of accelerometer data: a review of possible inferences <s> Location Tracking <s> The security and privacy risks posed by smartphone sensors such as microphones and cameras have been well documented. However, the importance of accelerometers have been largely ignored. We show that accelerometer readings can be used to infer the trajectory and starting point of an individual who is driving. This raises concerns for two main reasons. First, unauthorized access to an individual's location is a serious invasion of privacy and security. Second, current smartphone operating systems allow any application to observe accelerometer readings without requiring special privileges. We demonstrate that accelerometers can be used to locate a device owner to within a 200 meter radius of the true location. Our results are comparable to the typical accuracy for handheld global positioning systems. <s> BIB001 </s> Privacy implications of accelerometer data: a review of possible inferences <s> Location Tracking <s> Motion sensors, especially accelerometers, on smartphones have been discovered to be a powerful side channel for spying on users’ privacy. In this paper, we reveal a new accelerometer-based side-channel attack which is particularly serious: malware on smartphones can easily exploit the accelerometers to trace metro riders stealthily. We first address the challenge to automatically filter out metro-related data from a mass of miscellaneous accelerometer readings, and then propose a basic attack which leverages an ensemble interval classifier built from supervised learning to infer the riding trajectory of the user. As the supervised learning requires the attacker to collect labeled training data for each station interval, this attack confronts the scalability problem in big cities with a huge metro network. We thus further present an improved attack using semi-supervised learning, which only requires the attacker to collect labeled data for a very small number of distinctive station intervals. We conduct real experiments on a large self-built dataset, which contains more than 120 h of data collected from six metro lines of three major cities. The results show that the inferring accuracy could reach 89% and 94% if the user takes the metro for four and six stations, respectively. We finally discuss possible countermeasures against the proposed attack. <s> BIB002
It has been shown that accelerometers in mobile devices can be exploited for user localization and reconstruction of travel trajectories, even when other localization systems, such as GPS, are disabled. In BIB001 , Han et al. were able to geographically track a person who is driving a car based solely on accelerometer readings from the subject's smartphone. In their approach, they first calculate the vehicle's approximate motion trajectory using three-axis acceleration measurements from an iPhone located inside the vehicle, and then map the derived trajectory to the shape of existing routes on a map. An example application of the algorithm is displayed in Fig. 2 . Han et al. describe their results as "comparable to the typical accuracy for handheld global positioning systems." Hua, Shen and Zhong found that accelerometers in smartphones can also reveal the device's location while the holder is using a metropolitan train system BIB002 . To achieve this, the researchers compare and match acceleration patterns with labeled training data to recognize specific station intervals through which the user travels. Results from experiments on a real metro line show that the accuracy of their approach could reach up to 89% and 92% if the metro ride is longer than 3 or 5 stations, respectively BIB002 .