Moves like Jagger: Exploiting variations in instantaneous gait for spontaneous device pairing

Schürmann, Dominik; Brüsch, Arne; Nguyen, Ngu; Sigg, Stephan; Wolf, Lars

doi:10.1016/j.pmcj.2018.03.006

Cited by 12 publications

(28 citation statements)

References 41 publications

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“…Other advanced effort study comprise control of the speed, step-length, thigh lift, hip movement and width of steps [158] (E2, C2; advanced effort), intensively training individuals over multiple days [193] (E2, C2; advanced effort) or exploiting a 100+ subject database of gait sequences [101,102] (E1, C2; advanced effort). In addition, the high accuracy of video-based gait recognition systems also empowers an adversary to generate a database of gait information on multiple subjects unnoticed [236].…”

Section: Application Usage Patternsmentioning

confidence: 99%

Adversary Models for Mobile Device Authentication

Mayrhofer

Sigg

2021

ACM Comput. Surv.

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Mobile device authentication has been a highly active research topic for over 10 years, with a vast range of methods proposed and analyzed. In related areas, such as secure channel protocols, remote authentication, or desktop user authentication, strong, systematic, and increasingly formal threat models have been established and are used to qualitatively compare different methods. However, the analysis of mobile device authentication is often based on weak adversary models, suggesting overly optimistic results on their respective security. In this article, we introduce a new classification of adversaries to better analyze and compare mobile device authentication methods. We apply this classification to a systematic literature survey. The survey shows that security is still an afterthought and that most proposed protocols lack a comprehensive security analysis. The proposed classification of adversaries provides a strong and practical adversary model that offers a comparable and transparent classification of security properties in mobile device authentication.

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Section: Application Usage Patternsmentioning

confidence: 99%

Adversary Models for Mobile Device Authentication

Mayrhofer

Sigg

2021

ACM Comput. Surv.

Self Cite

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“…Careful choice of the parameters is therefore demanded to limit the advantage gained by an adversary through the use of fuzzy cryptography. From the protocols we investigated, BANDANA [11] and the IPI-protocol [12] employ BCH codes for error correction. [16] integrates the fuzzy vault design that operates on order-invariant tuples generated by Walkie-Talkie.…”

Section: Benefits and Pitfalls In Using Error Correctionmentioning

confidence: 99%

Security Properties of Gait for Mobile Device Pairing

Brüsch

Nguyen

Schürmann

et al. 2020

IEEE Trans. on Mobile Comput.

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Gait has been proposed as a feature for mobile device pairing across arbitrary positions on the human body. Results indicate that the correlation in gait-based features across different body locations is sufficient to establish secure device pairing. However, the population size of the studies is limited and powerful attackers with e.g. capability of video recording are not considered. We present a concise discussion of security properties of gait-based pairing schemes including quantization, classification and analysis of attack surfaces, of statistical properties of generated sequences, an entropy analysis, as well as possible threats and security weaknesses. For one of the schemes considered, we present modifications to fix an identified security flaw. As a general limitation of gait-based authentication or pairing systems, we further demonstrate that an adversary with video support can create key sequences that are sufficiently close to on-body generated acceleration sequences to breach gait-based security mechanisms.

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“…However, it relies on fuzzy cryptography which is more difficult and costly to implement. More recently, the proposal from [33] similarly uses fuzzy cryptography to extract common keys from accelerometer data. Two protocols for pairing based on accelerometer data are discussed in [25].…”

Section: A Related Workmentioning

confidence: 99%

Secure Accelerometer-Based Pairing of Mobile Devices in Multi-Modal Transport

et al. 2020

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We address the secure pairing of mobile devices based on accelerometer data under various transportation environments, e.g., train, tram, car, bike, walking, etc. As users commonly commute by several transportation modes, extracting session keys from various scenarios to secure the private network of user's devices or even the public network formed by devices belonging to distinct users that share the same location is crucial. The main goal of our work is to establish the amount of entropy that can be collected from these environments in order to determine concrete security bounds for each environment. We test several signal processing techniques on the extracted data, e.g., low-pass and high-pass filters, then apply sigma-delta modulation in order to expand the size of the feature vectors and increase both the pairing success rate and security level. Further, we bootstrap secure session keys by the use of existing cryptographic building blocks EKE (Encrypted Key Exchange) and SPEKE (Simple Password Exponential Key Exchange). We implement our proof-of-concept application on Android smart-phones and take benefit from numerical processing environments for the off-line analysis of the collected datasets.

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Moves like Jagger: Exploiting variations in instantaneous gait for spontaneous device pairing

Cited by 12 publications

References 41 publications

Adversary Models for Mobile Device Authentication

Adversary Models for Mobile Device Authentication

Security Properties of Gait for Mobile Device Pairing

Secure Accelerometer-Based Pairing of Mobile Devices in Multi-Modal Transport

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