Key Generation Based on Acceleration Data of Shaking Processes

Bichler, Daniel; Stromberg, G.; Huemer, Mario; Löw, Manuel

doi:10.1007/978-3-540-74853-3_18

Cited by 70 publications

(67 citation statements)

References 11 publications

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“…The correlation coefficient method is commonly used to compare the similarity of the shapes of two signals (e.g. [17]). Given two sequences A and B and Cov(A, B) denoting covariance between A and B, the correlation coefficient is computed as below:…”

Section: Feature Extractionmentioning

confidence: 99%

Stealing PINs via mobile sensors: actual risk versus user perception

Mehrnezhad

Toreini

Shahandashti

et al. 2017

Int. J. Inf. Secur.

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In this paper, we present the actual risks of stealing user PINs by using mobile sensors versus the perceived risks by users. First, we propose PINlogger.js which is a JavaScript-based side channel attack revealing user PINs on an Android mobile phone. In this attack, once the user visits a website controlled by an attacker, the JavaScript code embedded in the web page starts listening to the motion and orientation sensor streams without needing any permission from the user. By analysing these streams, it infers the user's PIN using an artificial neural network. Based on a test set of fifty 4-digit PINs, PINlogger.js is able to correctly identify PINs in the first attempt with a success rate of 74% which increases to 86 and 94% in the second and third attempts, respectively. The high success rates of stealing user PINs on mobile devices via JavaScript indicate a serious threat to user security. With the technical understanding of the information leakage caused by mobile phone sensors, we then study users' perception of the risks associated with these sensors. We design user studies to measure the general familiarity with different sensors and their functionality, and to investigate how concerned users are about their PIN being discovered by an app that has access to all these sensors. Our studies show that there is significant disparity between the actual and perceived levels of threat with regard to the compromise of the user PIN. We confirm our results by interviewing our participants using two different approaches, within-subject and between-subject, and compare the results. We discuss how this observation, along with other factors, renders many academic and industry solutions ineffective in preventing such side channel attacks. B Maryam Mehrnezhad

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Section: Feature Extractionmentioning

confidence: 99%

Stealing PINs via mobile sensors: actual risk versus user perception

Mehrnezhad

Toreini

Shahandashti

et al. 2017

Int. J. Inf. Secur.

View full text Add to dashboard Cite

show abstract

“…Our work shows a similar approach proposed in [4] and [5] which are, to our knowledge, the only two relevant articles 1 . The similarities between our work and [4], [5] lay on space dimension reduction using Euclidean norm, and both [4] as well as Protocol 2 in [5] prohibit exchange of acceleration data.…”

Section: Related Workmentioning

confidence: 87%

“…The feature extraction algorithms used in [5] includes coherence measure, which was originally introduced in [7], and quantized FFT coefficients. In [4], a similar approach in which acceleration signal was used for key generation was presented with a difference in using time domain acceleration features contrary to [5] where only frequency domain features were used. They claim that the same key would rather likely be generated for different shaking processes if the key generation was based on a frequency-based technique.…”

Section: Related Workmentioning

confidence: 99%

“…The similarities between our work and [4], [5] lay on space dimension reduction using Euclidean norm, and both [4] as well as Protocol 2 in [5] prohibit exchange of acceleration data. However, in [4] the signal is divided into segments from which a few principal components are extracted and used to learn the representation vectors. The key generation algorithm in [4] is based on pair wise nearest neighbour quantization.…”

Section: Related Workmentioning

confidence: 99%

“…It is a movement limited data channel between the two devices [2], [3]. Shaking process consists of fast up and down movements in the 3D space [4]. It is known that two devices that are shaken together will experience similar but not exactly the same movement patterns [5].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

ShakeMe: Key Generation from Shared Motion

Yuzuguzel

Niemi

Kıranyaz

et al. 2015

2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable,

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Abstract-Devices equipped with accelerometer sensors such as today's mobile devices can make use of motion to exchange information. A typical example for shared motion is shaking of two devices which are held together in one hand. Deriving a shared secret (key) from shared motion, e.g. for device pairing, is an obvious application for this. Only the keys need to be exchanged between the peers and neither the motion data nor the features extracted from it. This makes the pairing fast and easy. For this, each device generates an information signal (key) independently of each other and, in order to pair, they should be identical. The key is essentially derived by quantizing certain well discriminative features extracted from the accelerometer data after an implicit synchronization. In this paper, we aim at finding a small set of effective features which enable a significantly simpler quantization procedure than the prior art. Our tentative results with authentic accelerometer data show that this is possible with a competent accuracy (76%) and key strength (entropy approximately 15 bits).

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Mobile and Ubiquitous Computing

Yau

Huang

Gao

et al. 2008

Wiley Encyclopedia of Computer Science and Engineering

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The development of wireless communication technology and portable computing devices has led to mobile and ubiquitous computing, which has many applications from intelligent environmental control and smart home appliances, to interactive workspaces, mobile patient‐care systems, context‐aware tourist guides, and smart classrooms. In this article, an overview of mobile and ubiquitous computing is presented. The current state of research is summarized as follows wireless ad hoc networks, context‐and situation‐awareness in mobile and ubiquitous computing environments, and techniques for developing mobile and ubiquitous computing software.

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Key Generation Based on Acceleration Data of Shaking Processes

Cited by 70 publications

References 11 publications

Stealing PINs via mobile sensors: actual risk versus user perception

Stealing PINs via mobile sensors: actual risk versus user perception

ShakeMe: Key Generation from Shared Motion

Mobile and Ubiquitous Computing

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