A challenge in facilitating spontaneous mobile interactions is to provide pairing methods that are both intuitive and secure. Simultaneous shaking is proposed as a novel and easy-to-use mechanism for pairing of small mobile devices. The underlying principle is to use common movement as a secret that the involved devices share for mutual authentication. We present two concrete methods, ShaVe and ShaCK, in which sensing and analysis of shaking movement is combined with cryptographic protocols for secure authentication. ShaVe is based on initial key exchange followed by exchange and comparison of sensor data for verification of key authenticity. ShaCK, in contrast, is based on matching features extracted from the sensor data to construct a cryptographic key. The classification algorithms used in our approach are shown to robustly separate simultaneous shaking of two devices from other concurrent movement of a pair of devices, with a false negative rate of under 12 percent. A user study confirms that the method is intuitive and easy to use, as users can shake devices in an arbitrary pattern. Index Terms-Algorithm/protocol design and analysis, ubiquitous computing, mobile environments, authentication, human-centered computing, mobile applications.

Abstract. Small, mobile devices without user interfaces, such as Bluetooth headsets, often need to communicate securely over wireless networks. Active attacks can only be prevented by authenticating wireless communication, which is problematic when devices do not have any a priori information about each other. We introduce a new method for device-to-device authentication by shaking devices together. This paper describes two protocols for combining cryptographic authentication techniques with known methods of accelerometer data analysis to the effect of generating authenticated, secret keys. The protocols differ in their design, one being more conservative from a security point of view, while the other allows more dynamic interactions. Three experiments are used to optimize and validate our proposed authentication method.

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Today, mobile devices like smartphones and tablets have become an indispensable part of people's lives, posing many new questions e.g., in terms of interaction methods, but also security. In this paper, we conduct a large scale, long term analysis of mobile device usage characteristics like session length, interaction frequency, and daily usage in locked and unlocked state with respect to location context and diurnal pattern. Based on detailed logs from 29,279 mobile phones and tablets representing a total of 5,811 years of usage time, we identify and analyze 52.2 million usage sessions with some participants providing data for more than four years. Our results show that context has a highly significant effect on both frequency and extent of mobile device usage, with mobile phones being used twice as much at home compared to in the office. Interestingly, devices are unlocked for only 46 % of the interactions. We found that with an average of 60 interactions per day, smartphones are used almost thrice as often as tablet devices (23), while usage sessions on tablets are three times longer, hence are used almost for an equal amount of time throughout the day. We conclude that usage session characteristics differ considerably between tablets and smartphones. These results inform future approaches to mobile interaction as well as security.

Biometric gait authentication using Personal Mobile Device (PMD) based accelerometer sensors offers a user-friendly, unobtrusive, and periodic way of authenticating individuals on PMD. In this paper, we present a technique for gait cycle extraction by incorporating the Piecewise Linear Approximation (PLA) technique. We also present two new approaches to classify gait features extracted from the cycle-based segmentation by using Support Vector Machines (SVMs); a) pre-computed data matrix, b) pre-computed kernel matrix. In the first approach, we used Dynamic Time Warping (DTW) distance to compute data matrices, and in the later DTW is used for constructing an elastic similarity measure based kernel function called Gaussian Dynamic Time Warp (GDTW) kernel. Both approaches utilize the DTW similarity measure and can be used for classifying equal length gait cycles, as well as different length gait cycles. To evaluate our approaches we used normal walk biometric gait data of 51 participants. This gait data is collected by attaching a PMD to the belt around the waist, on the right-hand side of the hip. Results show that these new approaches need to be studied more, and potentially lead us to design more robust and reliable gait authentication systems using PMD based accelerometer sensor.