Checking More and Alerting Less: Detecting Privacy Leakages via Enhanced Data-flow Analysis and Peer Voting

Lu, Kangjie; Li, Zhichun; Kemerlis, Vasileios P.; Wu, Zhenyu; Lu, Long; Zheng, Cong; Qian, Zhiyun; Lee, Wenke; Jiang, Guofei

doi:10.14722/ndss.2015.23287

Cited by 70 publications

(47 citation statements)

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“…Most of these works are orthogonal to our system as their motivation and techniques differ. Examples of such work include, but are not limited to, recommending new frameworks/privacy metrics [35], [25], [36], [37], [38], [39], [40], location obfuscation [1], [2], [3], [4], [5], location cloaking [6], generating dummy locations [7], [8], [9], [10], [11], [41], tainting sensitive data [42], [43], dynamic analysis [44], [45], static code analysis [46], [47], [48], [49], permissions analysis [50], and application retrofitting [51], [52], [53].…”

Section: Related Workmentioning

confidence: 99%

Mitigating Location Privacy Attacks on Mobile Devices using Dynamic App Sandboxing

Narain

Noubir

2019

Proceedings on Privacy Enhancing Technologies

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We present the design, implementation and evaluation of a system, called MATRIX, developed to protect the privacy of mobile device users from location inference and sensor side-channel attacks. MATRIX gives users control and visibility over location and sensor (e.g., Accelerometers and Gyroscopes) accesses by mobile apps. It implements a PrivoScope service that audits all location and sensor accesses by apps on the device and generates real-time notifications and graphs for visualizing these accesses; and a Synthetic Location service to enable users to provide obfuscated or synthetic location trajectories or sensor traces to apps they find useful, but do not trust with their private information. The services are designed to be extensible and easy for users, hiding all of the underlying complexity from them. MATRIX also implements a Location Provider component that generates realistic privacy-preserving synthetic identities and trajectories for users by incorporating traffic information using historical data from Google Maps Directions API, and accelerations using statistical information from user driving experiments. The random traffic patterns are generated by modeling/solving user schedule using a randomized linear program and modeling/solving for user driving behavior using a quadratic program. We extensively evaluated MATRIX using user studies, popular location-driven apps and machine learning techniques, and demonstrate that it is portable to most Android devices globally, is reliable, has low-overhead, and generates synthetic trajectories that are difficult to differentiate from real mobility trajectories by an adversary.

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Section: Related Workmentioning

confidence: 99%

Mitigating Location Privacy Attacks on Mobile Devices using Dynamic App Sandboxing

Narain

Noubir

2019

Proceedings on Privacy Enhancing Technologies

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“…AsDroid [22] leverages the inconsistency between the code and GUI to detect malicious behaviors. AAPL [28] examines inconsistent behaviors within similar functionalities of similar apps to detect privacy leaks. Our work is fundamentally different from all these efforts, as we do not focus on apps nor specific services.…”

Section: Related Workmentioning

confidence: 99%

AceDroid: Normalizing Diverse Android Access Control Checks for Inconsistency Detection

Aafer

Huang

Sun

et al. 2018

Proceedings 2018 Network and Distributed System Security Symposium

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Abstract-The Android framework has raised increased security concerns with regards to its access control enforcement. Particularly, existing research efforts successfully demonstrate that framework security checks are not always consistent across appaccessible APIs. However, existing efforts fall short in addressing peculiarities that characterize the complex Android access control and the diversity introduced by the heavy vendor customization. In this paper, we develop a new analysis framework AceDroid that models Android access control in a path-sensitive manner and normalizes diverse checks to a canonical form. We applied our proposed modeling to perform inconsistency analysis for 12 images. Our tool proved to be quite effective, enabling to detect a significant number of inconsistencies introduced by various vendors and to suppress substantial false alarms. Through investigating the results, we uncovered high impact attacks enabling to write a key logger, send premium sms messages, bypass user restrictions, perform a major denial of services and other critical operations.

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“…AAPL is an automated system for discovering privacy leaks. This system is based on static analysis techniques, such as conditional flow identification and joint flow tracking.…”

Section: Related Workmentioning

confidence: 99%

VAnDroid: A framework for vulnerability analysis of Android applications using a model‐driven reverse engineering technique

2018

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Summary Android is extensively used worldwide by mobile application developers. Android provides applications with a message passing system to communicate within and between them. Due to the risks associated with this system, it is vital to detect its unsafe operations and potential vulnerabilities. To achieve this goal, a new framework, called VAnDroid, based on Model Driven Reverse Engineering (MDRE), is presented that identifies security risks and vulnerabilities related to the Android application communication model. In the proposed framework, some security‐related information included in an Android app is automatically extracted and represented as a domain‐specific model. Then, it is used for analyzing security configurations and identifying vulnerabilities in the corresponding application. The proposed framework is implemented as an Eclipse‐based tool, which automatically identifies the Intent Spoofing and Unauthorized Intent Receipt as two attacks related to the Android application communication model. To evaluate the tool, it has been applied to several real‐world Android applications, including 20 apps from Google Play and 110 apps from the F‐Droid repository. VAnDroid is also compared with several existing analysis tools, and it is shown that it has a number of key advantages over those tools specifically regarding its high correctness, scalability, and usability in discovering vulnerabilities. The results well indicate the effectiveness and capacity of the VAnDroid as a promising approach in the field of Android security.

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Checking More and Alerting Less: Detecting Privacy Leakages via Enhanced Data-flow Analysis and Peer Voting

Cited by 70 publications

References 0 publications

Mitigating Location Privacy Attacks on Mobile Devices using Dynamic App Sandboxing

Mitigating Location Privacy Attacks on Mobile Devices using Dynamic App Sandboxing

AceDroid: Normalizing Diverse Android Access Control Checks for Inconsistency Detection

VAnDroid: A framework for vulnerability analysis of Android applications using a model‐driven reverse engineering technique

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