PiOS: Detecting Privacy Leaks in iOS Applications

By Manuel Egele, Christopher Krügel, Engin Kirda, Giovanni Vigna. Network and Distributed System Security Symposium, 2011

591 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/daaf88229fe086b6fc99ccaf189f647be9018a30

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Citations in the corpus

AppsPlayground: automatic security analysis of smartphone applications (2013, background): “Most of these applications are created by unknown developers who may not operate in the users’ best interests, leading to malware [14, 16] and frequent exposure of privacy sensitive information such as phone identifiers and location [6, 7, 8].”
Unsafe exposure analysis of mobile in-app advertisements (2012, methodology): “PiOS: Detecting Privacy Leaks in iOS Applications.”
: Privacy on Mobile Devices – It ’ s Complicated (2016, methodology): “PiOS uses static analysis on iOS applications to detect code flows which first access user data and then utilize that data in network API calls [153].”
Hey, You, Get Off of My Market: Detecting Malicious Apps in Official and Alternative Android Markets (2012, background): “For example, TaintDroid [35] and PiOS [34] are two representative systems that demonstrate potential privacy threats from third-party apps in both Android and iOS platforms.”
A Survey on Security for Mobile Devices (2013, background): “[88] study the privacy threats that applications for Apples iOS pose to users.”
ReCon: Revealing and Controlling PII Leaks in Mobile Network Traffic (2015, background): “Apps extensively track users and leak their personally identiﬁable information (PII) [17, 23, 27, 35, 58], and users are generally unaware and unable to stop them [21, 29].”
(sp)iPhone: decoding vibrations from nearby keyboards using mobile phone accelerometers (2011, background): “Categories and Subject Descriptors K.6.5 [Management of Computing and Information Systems]: Security and Protection invasive software General Terms Security Keywords mobile phones, information leakage, accelerometer 1.”
AndroidLeaks: Automatically Detecting Potential Privacy Leaks in Android Applications on a Large Scale (2012, methodology): “AndroidLeaks and PiOS both found privacy leaks related to device ID, location and phone number.”
PUMA: programmable UI-automation for large-scale dynamic analysis of mobile apps (2014, methodology): “Static analysis of app binaries, as used in prior work to identify privacy [21] and security [10, 12] problems, or app clones [9] etc. , can scale to a large number of apps.”
Expectation and purpose: understanding users' mental models of mobile app privacy through crowdsourcing (2012, methodology): “Researchers have also developed many useful techniques and tools to detect the sensitive information leakage in mobile apps [3, 10, 12-16, 19, 35, 36], by using permission analysis (e.g. [3, 16]), static code analysis (e.g. [12]), network analysis (e.g. [35]), or dynamic flow analysis (e.g. [14]).”
Privacy Protection for Preventing Data Over-Collection in Smart City (2016, background): “Sharing a similar goal with PiOS, Enck et al. proposed TaintDroid [3], a system-wide dynamic taint tracking multiple sources of sensitive data.”
PScout: analyzing the Android permission specification (2012, methodology): “PiOS [13] is a tool that uses static analysis to detect data flows that lead to privacy leakage in iOS applications.”
SoK: Privacy on Mobile Devices – It’s Complicated (2016, methodology): “PiOS uses static analysis on iOS applications to detect code flows which first access user data and then utilize that data in network API calls [153].”
Uranine: Real-time Privacy Leakage Monitoring without System Modification for Android (2015, methodology): “PiOS [11] uses it to detect privacy leaks on iOS apps.”
AccelPrint: Imperfections of Accelerometers Make Smartphones Trackable (2014, background): “Sensor-rich smartphones and tablets are increasingly becoming the target of attacks for harvesting sensitive data [24].”
Dissecting Android Malware: Characterization and Evolution (2012, background): “TaintDroid [34] and PiOS [35] are two systems that expose possible privacy leaks on Android and iOS, respectively.”
Apposcopy: semantics-based detection of Android malware through static analysis (2014, background): “For example, TaintDroid [17] and Vet-Droid [37] are dynamic taint analyses that track information ﬂow by instrumenting the Dalvik VM, and examples of static taint analyses include [20, 21, 23, 16].”
XManDroid: A New Android Evolution to Mitigate Privilege Escalation Attacks (2011, methodology): “PiOS is a static analysis system to detect leakage of private data by applications for Apple’s iOS operating system [10].”
Detection of Intrusions and Malware, and Vulnerability Assessment (2012, background): “The separation is enforced by the chipset [21].”