Privacy implications of accelerometer data: a review of possible inferences <s> User Identification <s> Many applications benefit from user location data, but location data raises privacy concerns. Anonymization can protect privacy, but identities can sometimes be inferred from supposedly anonymous data. This paper studies a new attack on the anonymity of location data. We show that if the approximate locations of an individual's home and workplace can both be deduced from a location trace, then the median size of the individual's anonymity set in the U.S. working population is 1, 21 and 34,980, for locations known at the granularity of a census block, census track and county respectively. The location data of people who live and work in different regions can be re-identified even more easily. Our results show that the threat of re-identification for location data is much greater when the individual's home and work locations can both be deduced from the data. To preserve anonymity, we offer guidance for obfuscating location traces before they are disclosed. <s> BIB001 </s> Privacy implications of accelerometer data: a review of possible inferences <s> User Identification <s> Mobile devices are becoming increasingly sophisticated and now incorporate many diverse and powerful sensors. The latest generation of smart phones is especially laden with sensors, including GPS sensors, vision sensors (cameras), audio sensors (microphones), light sensors, temperature sensors, direction sensors (compasses), and acceleration sensors. In this paper we describe and evaluate a system that uses phone-based acceleration sensors, called accelerometers, to identify and authenticate cell phone users. This form of behavioral biometrie identification is possible because a person's movements form a unique signature and this is reflected in the accelerometer data that they generate. To implement our system we collected accelerometer data from thirty-six users as they performed normal daily activities such as walking, jogging, and climbing stairs, aggregated this time series data into examples, and then applied standard classification algorithms to the resulting data to generate predictive models. These models either predict the identity of the individual from the set of thirty-six users, a task we call user identification, or predict whether (or not) the user is a specific user, a task we call user authentication. This work is notable because it enables identification and authentication to occur unobtrusively, without the users taking any extra actions-all they need to do is carry their cell phones. There are many uses for this work. For example, in environments where sharing may take place, our work can be used to automatically customize a mobile device to a user. It can also be used to provide device security by enabling usage for only specific users and can provide an extra level of identity verification. <s> BIB002 </s> Privacy implications of accelerometer data: a review of possible inferences <s> User Identification <s> In modern mobile networks, users increasingly share their location with third-parties in return for location-based services. Previous works show that operators of location-based services may identify users based on the shared location information even if users make use of pseudonyms. In this paper, we push the understanding of the privacy risk further. We evaluate the ability of location-based services to identify users and their points of interests based on different sets of location information. We consider real life scenarios of users sharing location information with location-based services and quantify the privacy risk by experimenting with real-world mobility traces. <s> BIB003 </s> Privacy implications of accelerometer data: a review of possible inferences <s> User Identification <s> Accelerometer-based biometric gait recognition offers a convenient way to authenticate users on their mobile devices. Modern smartphones contain in-built accelerometers which can be used as sensors to acquire the necessary data while the subjects are walking. Hence, no additional costs for special sensors are imposed to the user. In this publication we extract several features from the gait data and use the k-Nearest Neighbour algorithm for classification. We show that this algorithm yields a better biometric performance than the machine learning algorithms we previously used for classification, namely Hidden Markov Models and Support Vector Machines. We implemented the presented method on a smartphone and demonstrate that it is efficient enough to be applied in practice. <s> BIB004 </s> Privacy implications of accelerometer data: a review of possible inferences <s> User Identification <s> While body movement patterns recorded by a smartphone accelerometer are now well understood to be discriminative enough to separate users, little work has been done to address the question of if or how the position in which the phone is held affects user authentication. In this work, we show through a combination of supervised learning methods and statistical tests, that there are certain users for whom exploitation of information of how a phone is held drastically improves classification performance. We propose a two-stage authentication framework that identifies the location of the phone before performing authentication, and show its benefits based on a dataset of 30 users. Our work represents a first step towards bridging the gap between accelerometer-based authentication systems analyzed from the context of a laboratory environment and a real accelerometer-based authentication system in the wild where phone positioning cannot be assumed. <s> BIB005 </s> Privacy implications of accelerometer data: a review of possible inferences <s> User Identification <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> BIB006 </s> Privacy implications of accelerometer data: a review of possible inferences <s> User Identification <s> In this paper, we present the design, implementation and evaluation of a user authentication system, Headbanger, for smart head-worn devices, through monitoring the user's unique head-movement patterns in response to an external audio stimulus. Compared to today's solutions, which primarily rely on indirect authentication mechanisms via the user's smartphone, thus cumbersome and susceptible to adversary intrusions, the proposed head-movement based authentication provides an accurate, robust, light-weight and convenient solution. Through extensive experimental evaluation with 95 participants, we show that our mechanism can accurately authenticate users with an average true acceptance rate of 95.57% while keeping the average false acceptance rate of 4.43%. We also show that even simple head-movement patterns are robust against imitation attacks. Finally, we demonstrate our authentication algorithm is rather light-weight: the overall processing latency on Google Glass is around 1.9 seconds. <s> BIB007
Body movement patterns recorded by accelerometers in mobile devices have been demonstrated to be discriminative enough to differentiate between, or even uniquely identify, users. Various accelerometer-only approaches have been proposed to confirm the identity of a user based on biometric gait features BIB004 BIB005 , hand gestures  , or head movements BIB007 . Using accelerometer rea-dings from smartphones, Kwapisz, Weiss and Moore were able to recognize individuals from a pool of 36 test subjects with 100% accuracy BIB002 . It has also been shown that, through aerial vibrations, accelerometers can be sensitive enough to capture sound, including human speech, in sufficient quality to distinguish between different speakers with high accuracy [35] . The location trajectory of a mobile device, which can be inferred from accelerometer data under certain conditions (as explained in section 2.2), may reveal a user's work and home addresses BIB003 , and -in conjunction with white pages, employment directories, tax records, or other auxiliary datasets -a user's real identity BIB001 . Following an approach commonly referred to as device fingerprinting, users can further be told apart based on unique characteristics and features of their personal devices. Calibration errors in accelerometers, which are caused by imperfections in the manufacturing process, have been found sufficient to uniquely identify their encapsulating device BIB006  . Such a "fingerprint" can be used, for instance, to track users across repeated website visits, even when private browsing is activated and other tracking technologies, such as canvas fingerprinting or cookies, are blocked  .