Analyzing User Awareness of Privacy Data Leak in Mobile Applications

Kim, Young‐Ho; Oh, Tae Hwan; Kim, Jeong-Nyeo

doi:10.1155/2015/369489

Cited by 24 publications

(15 citation statements)

References 13 publications

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“…Smartphone have become omnipresent personal computing platforms for users to access Internet services whenever and wherever. Recent surveys [ 1 , 2 ] show that as more and more private information and security information are stored in smartphones (e.g., 92.8% of Android smartphone users store private information in their smartphones), the risk of information leakage is becoming a major concern for the information society [ 3 ]. The most common mechanism to address this problem is authentication.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Motion-Sensor Behavior for User Authentication on Smartphones

Shen

Yuan

et al. 2016

Sensors

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The growing trend of using smartphones as personal computing platforms to access and store private information has stressed the demand for secure and usable authentication mechanisms. This paper investigates the feasibility and applicability of using motion-sensor behavior data for user authentication on smartphones. For each sample of the passcode, sensory data from motion sensors are analyzed to extract descriptive and intensive features for accurate and fine-grained characterization of users’ passcode-input actions. One-class learning methods are applied to the feature space for performing user authentication. Analyses are conducted using data from 48 participants with 129,621 passcode samples across various operational scenarios and different types of smartphones. Extensive experiments are included to examine the efficacy of the proposed approach, which achieves a false-rejection rate of 6.85% and a false-acceptance rate of 5.01%. Additional experiments on usability with respect to passcode length, sensitivity with respect to training sample size, scalability with respect to number of users, and flexibility with respect to screen size were provided to further explore the effectiveness and practicability. The results suggest that sensory data could provide useful authentication information, and this level of performance approaches sufficiency for two-factor authentication on smartphones. Our dataset is publicly available to facilitate future research.

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Section: Introductionmentioning

confidence: 99%

Performance Analysis of Motion-Sensor Behavior for User Authentication on Smartphones

Shen

Yuan

et al. 2016

Sensors

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“…POWER CONSUMPTION UNDER FOUR DIFFERENT SCENARIOS. To measure the battery consumption, we consider the following four testing scenarios:(1) Phone is locked (i.e., not being used) and SmarterYou is off. (2) Phone is locked and SmarterYou keeps running.…”

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confidence: 99%

Implicit Smartphone User Authentication with Sensors and Contextual Machine Learning

Lee

2017

2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)

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Abstract-Authentication of smartphone users is important because a lot of sensitive data is stored in the smartphone and the smartphone is also used to access various cloud data and services. However, smartphones are easily stolen or co-opted by an attacker. Beyond the initial login, it is highly desirable to re-authenticate end-users who are continuing to access securitycritical services and data. Hence, this paper proposes a novel authentication system for implicit, continuous authentication of the smartphone user based on behavioral characteristics, by leveraging the sensors already ubiquitously built into smartphones. We propose novel context-based authentication models to differentiate the legitimate smartphone owner versus other users. We systematically show how to achieve high authentication accuracy with different design alternatives in sensor and feature selection, machine learning techniques, context detection and multiple devices. Our system can achieve excellent authentication performance with 98.1% accuracy with negligible system overhead and less than 2.4% battery consumption.

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“…Malicious code can wreak havoc on IoT and mobile devices [7]. The functioning of installed applications in mobile devices will also be negatively affected in the form of data breaches or privacy leakage.…”

Section: Introductionmentioning

confidence: 99%

PlusApps: Towards a Privacy Risk Analysis for Android Plus Applications

Alzahrani¹

2020

IJACSA

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The Android platform leads the mobile operating system marketplace and subsequently has drawn the interest of malware authors and researchers. The significant number of proposed malware detection techniques, classification models and practical reverse engineering solutions are insufficient and there is a lack of perfection. Also, the number of Android apps has increased significantly in recent years, as has the number of apps revealing confidential data. It is essential to investigate the applications and make sure that none of them are leaking privacy data, and consequently a privacy leak analysis approach is needed. Therefore, this paper investigates plus apps behavior and data leakages with a machine-learning algorithm to determine the best features for differentiating plus apps from original apps. The result of the analysis discloses that the SVM classifier presents the greatest accuracy. Further investigation demonstrates that the classifier with the ranking algorithm that uses correlation coefficient (CorEvel) and information gain (InfGain) methods offers more exceptional precision than the other correlation algorithms. The result of this experiment proves that the ranking algorithm is able to decrease the dimension of features and produce an accuracy of 96.60%.

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Analyzing User Awareness of Privacy Data Leak in Mobile Applications

Cited by 24 publications

References 13 publications

Performance Analysis of Motion-Sensor Behavior for User Authentication on Smartphones

Performance Analysis of Motion-Sensor Behavior for User Authentication on Smartphones

Implicit Smartphone User Authentication with Sensors and Contextual Machine Learning

PlusApps: Towards a Privacy Risk Analysis for Android Plus Applications

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