User-generated free-form gestures for authentication

Sherman, Michael; Clark, Gradeigh D.; Yang, Yan; Sugrim, Shridatt; Modig, Arttu; Lindqvist, Janne; Oulasvirta, Antti; Roos, Teemu

doi:10.1145/2594368.2594375

Cited by 94 publications

(95 citation statements)

References 39 publications

(78 reference statements)

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“…Their scheme used mutual information to select features that give high information gain. Sherman et al [32] studied free-form multitouch gestures for mobile authentication. A part of their work used mutual information to illustrate the security strength of free-form gestures.…”

Section: Related Workmentioning

confidence: 99%

Touch and You’re Trapp(ck)ed: Quantifying the Uniqueness of Touch Gestures for Tracking

Masood

Zhao

Asghar

et al. 2018

Proceedings on Privacy Enhancing Technologies

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Abstract:We argue that touch-based gestures on touchscreen devices enable the threat of a form of persistent and ubiquitous tracking which we call touch-based tracking. Touch-based tracking goes beyond the tracking of virtual identities and has the potential for cross-device tracking as well as identifying multiple users using the same device. We demonstrate the likelihood of touchbased tracking by focusing on touch gestures widely used to interact with touch devices such as swipes and taps.. Our objective is to quantify and measure the information carried by touch-based gestures which may lead to tracking users. For this purpose, we develop an information theoretic method that measures the amount of information about users leaked by gestures when modelled as feature vectors. Our methodology allows us to evaluate the information leaked by individual features of gestures, samples of gestures, as well as samples of combinations of gestures. Through our purposebuilt app, called TouchTrack, we gather gesture samples from 89 users, and demonstrate that touch gestures contain sufficient information to uniquely identify and track users. Our results show that writing samples (on a touch pad) can reveal 73.7% of information (when measured in bits), and left swipes can reveal up to 68.6% of information. Combining different combinations of gestures results in higher uniqueness, with the combination of keystrokes, swipes and writing revealing up to 98.5% of information about users. We further show that, through our methodology, we can correctly re-identify returning users with a success rate of more than 90%.

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Section: Related Workmentioning

confidence: 99%

Touch and You’re Trapp(ck)ed: Quantifying the Uniqueness of Touch Gestures for Tracking

Masood

Zhao

Asghar

et al. 2018

Proceedings on Privacy Enhancing Technologies

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“…As mobile devices are becoming ubiquitous, recent years have witnessed growing research interest in applying behavioral biometrics to mobile authentication. Many behavioral biometrics studies have been conducted based on phone touch operations [5], [29], [32], PIN and unlock pattern operations [4], [11], online signatures [19], [10], multitouch gestures [18], [21], [22] and three-dimensional gestures [13], [30], [28]. …”

Section: Related Workmentioning

confidence: 99%

“…Compared to physiological biometrics, behavioral biometrics, which is embedded in humans’ behavior, is more difficult to steal or mimic and therefore causes less privacy concerns. Among growing interests in applying behavioral biometrics to security and privacy protection, gesture or motion based behavioral biometric authentication has attracted a great deal of research efforts (e.g., [1], [4], [18], [21], [24], [22], [19], [32], [30], [3]).…”

Section: Introductionmentioning

confidence: 99%

SEGAUTH: A segment-based approach to behavioral biometric authentication

Xie

Bian

2016

2016 IEEE Conference on Communications and Network Security (CNS)

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Many studies have been conducted to apply behavioral biometric authentication on/with mobile devices and they have shown promising results. However, the concern about the verification accuracy of behavioral biometrics is still common given the dynamic nature of behavioral biometrics. In this paper, we address the accuracy concern from a new perspective—behavior segments, that is, segments of a gesture instead of the whole gesture as the basic building block for behavioral biometric authentication. With this unique perspective, we propose a new behavioral biometric authentication method called SegAuth, which can be applied to various gesture or motion based authentication scenarios. SegAuth can achieve high accuracy by focusing on each user’s distinctive gesture segments that frequently appear across his or her gestures. In SegAuth, a time series derived from a gesture/motion is first partitioned into segments and then transformed into a set of string tokens in which the tokens representing distinctive, repetitive segments are associated with higher genuine probabilities than those tokens that are common across users. An overall genuine score calculated from all the tokens derived from a gesture is used to determine the user’s authenticity. We have assessed the effectiveness of SegAuth using 4 different datasets. Our experimental results demonstrate that SegAuth can achieve higher accuracy consistently than existing popular methods on the evaluation datasets.

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“…Sherman et al [21] studies the security and memorability of using userdefined free-form multi-touch gestures for authentication. These forms of data have rich biometric characteristics of the user.…”

Section: Related Workmentioning

confidence: 99%

Gesture On

Lü

2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

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A significant percentage of mobile interaction involves short-period usages that originate from the standby modeusers wake up a device by pressing the power button, unlock the device by authenticating themselves, and then search for a target app or functionality on the device. These additional steps preceding a target task imposes significant overhead on users for each mobile device access. To address the issue, we developed Gesture On, a system that enables gesture shortcuts in the standby mode by which a user can draw a gesture on the touchscreen before the screen is turned on. Based on the gesture, our system directly brings up a target item onto the screen that bypasses all these additional steps in a mobile access. This paper examines several challenges in realizing Gesture On, including robustly rejecting accidental touches when the device is in standby, battery consumption incurred for continuous sensing and gesture-based user authentication methods for automatically device unlocking. Our analyses based on a set of user data indicated that Gesture On demonstrates a feasible approach for leveraging the standby mode for fast access to mobile content.

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User-generated free-form gestures for authentication

Cited by 94 publications

References 39 publications

Touch and You’re Trapp(ck)ed: Quantifying the Uniqueness of Touch Gestures for Tracking

Touch and You’re Trapp(ck)ed: Quantifying the Uniqueness of Touch Gestures for Tracking

SEGAUTH: A segment-based approach to behavioral biometric authentication

Gesture On

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