Whose move is it anyway? Authenticating smart wearable devices using unique head movement patterns

Li, Sugang; Ashok, Ashwin; Zhang, Yanyong; Xu, Chenren; Lindqvist, Janne; Gruteser, Macro

doi:10.1109/percom.2016.7456514

Cited by 89 publications

(72 citation statements)

References 32 publications

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“…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 [40,41], hand gestures [42], or head movements [43]. 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 [44].…”

Section: User Identificationmentioning

confidence: 99%

Privacy implications of accelerometer data

Kröger

Raschke

Bhuiyan

2019

Proceedings of the 3rd International Conference on Cryptography, Security and Privacy

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Accelerometers are sensors for measuring acceleration forces. They can be found embedded in many types of mobile devices, including tablet PCs, smartphones, and smartwatches. Some common uses of built-in accelerometers are automatic image stabilization, device orientation detection, and shake detection. In contrast to sensors like microphones and cameras, accelerometers are widely regarded as not privacy-intrusive. This sentiment is reflected in protection policies of current mobile operating systems, where third-party apps can access accelerometer data without requiring security permission. It has been shown in experiments, however, that seemingly innocuous sensors can be used as a side channel to infer highly sensitive information about people in their vicinity. Drawing from existing literature, we found that accelerometer data alone may be sufficient to obtain information about a device holder's location, activities, health condition, body features, gender, age, personality traits, and emotional state. Acceleration signals can even be used to uniquely identify a person based on biometric movement patterns and to reconstruct sequences of text entered into a device, including passwords. In the light of these possible inferences, we suggest that accelerometers should urgently be re-evaluated in terms of their privacy implications, along with corresponding adjustments to sensor protection mechanisms.

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Section: User Identificationmentioning

confidence: 99%

Privacy implications of accelerometer data

Kröger

Raschke

Bhuiyan

2019

Proceedings of the 3rd International Conference on Cryptography, Security and Privacy

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“…As we use a wrapper-based approach, running the classifier is an inherent part in feature selection. The algorithm therefore calls the Get z-List algorithm (Algorithm 2) as a subroutine (based on a similar procedure from [32]). This algorithm computes the z values that give TPR of 1 and the least FPR for each possible feature subset.…”

Section: F Feature Selectionmentioning

confidence: 99%

BehavioCog: An Observation Resistant Authentication Scheme

Chauhan

Zhao

Asghar

et al. 2017

Financial Cryptography and Data Security

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We propose that by integrating behavioural biometric gestures-such as drawing figures on a touch screenwith challenge-response based cognitive authentication schemes, we can benefit from the properties of both. On the one hand, we can improve the usability of existing cognitive schemes by significantly reducing the number of challenge-response rounds by (partially) relying on the hardness of mimicking carefully designed behavioural biometric gestures. On the other hand, the observation resistant property of cognitive schemes provides an extra layer of protection for behavioural biometrics; an attacker is unsure if a failed impersonation is due to a biometric failure or a wrong response to the challenge. We design and develop an instantiation of such a "hybrid" scheme, and call it BehavioCog. To provide security close to a 4-digit PIN-one in 10,000 chance to impersonate-we only need two challenge-response rounds, which can be completed in less than 38 seconds on average (as estimated in our user study), with the advantage that unlike PINs or passwords, the scheme is secure under observation.

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“…In this way, approaches, which exploit biometrics, like fingerprint recognition, face recognition, iris recognition, retina recognition, hand recognition or even dynamic behavior such as voice recognition, gait patterns or even keystroke dynamics, can help detect imposters in real time [10]. Every new authentication method comes with a possible risk of low user acceptance due to latency and increasing complexity [11].…”

Section: Introductionmentioning

confidence: 99%

Authentication schemes for smart mobile devices: threat models, countermeasures, and open research issues

et al. 2019

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This paper presents a comprehensive investigation of authentication schemes for smart mobile devices. We start by providing an overview of existing survey articles published in the recent years that deal with security for mobile devices. Then, we give a classification of threat models in smart mobile devices in five categories, including, identity-based attacks, eavesdropping-based attacks, combined eavesdropping and identity-based attacks, manipulation-based attacks, and service-based attacks. This is followed by a description of multiple existing threat models. We also provide a classification of countermeasures into four types of categories, including, cryptographic functions, personal identification, classification algorithms, and channel characteristics. Therefore, according to the countermeasure characteristic used and the authentication model, we categorize the authentication schemes for smart mobile devices in four categories, namely, 1) biometric-based authentication schemes, 2) channelbased authentication schemes, 3) factors-based authentication schemes, and 4) ID-based authentication schemes. In addition, we provide a taxonomy and comparison of authentication schemes for smart mobile devices in form of tables. Finally, we identify open challenges and future research directions.To conduct the literature review, we followed the same process used in our previous work [14].Specifically, the identification of literature for analysis in this paper was based on a keyword search, namely, "authentication scheme", "authentication protocol", "authentication system", and "authentication framework". By searching these keywords in academic databases such as SCO-PUS, Web of Science, IEEE Xplore Digital Library, and ACM Digital Library, an initial set of relevant sources were located. Firstly, only proposed authentication schemes for smart mobile devices were collected. Secondly, each collected source was evaluated against the following criteria: 1) reputation, 2) relevance, 3) originality, 4) date of publication (between 2007 and 2018), and 5) most influential papers in the field. The final pool of papers consists of the most important papers in the field of mobile devices that focus on the authentication as their objective.Our search started on 01/11/2017 and continued until the submission date of this paper. The main contributions of this paper are:• We discuss the existing surveys on security for smart mobile devices.• We classify the threat models, which are considered by the authentication schemes in smart mobile devices, into five main categories, namely, identity-based attacks, eavesdropping-based attacks, combined eavesdropping and identity-based attacks, manipulationbased attacks, and service-based attacks.• We review existing research on countermeasures and security analysis techniques in smart mobile devices.• We provide a taxonomy and a side-by-side comparison, in a tabular form, of the stateof-the-art on the recent advancements towards secure and authentication schemes in smart mobile devices with respec...

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Whose move is it anyway? Authenticating smart wearable devices using unique head movement patterns

Cited by 89 publications

References 32 publications

Privacy implications of accelerometer data

Privacy implications of accelerometer data

BehavioCog: An Observation Resistant Authentication Scheme

Authentication schemes for smart mobile devices: threat models, countermeasures, and open research issues

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