iABC: Towards a hybrid framework for analyzing and classifying behaviour of iOS applications using static and dynamic analysis

Bhatt, Arpita Jadhav; Gupta, Chetan; Mittal, Sangeeta

doi:10.1016/j.jisa.2018.07.005

Cited by 10 publications

(12 citation statements)

References 7 publications

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“…Our previous works on dynamic analysis of run-time behavior of some iOS apps have revealed startling facts about how user's data is shared over the network [6,25]. In this work, we extend and enhance our previous work on dynamic analysis [6,24] and propose privacy preserving approach to decrease privacy disclosure score of an individual user. To determine the data types of the attributes that are shared over the network, an exhaustive dynamic analysis was performed for different permissions to list all types of data being shared after user grants permissions to the application.…”

Section: Ishield: Privacy Score Modelmentioning

confidence: 93%

“…Many background studies and our previous work on dynamic analysis has revealed that most of the apps share user's data in clear text over the network [24]. The privacy concerns get multiplexed because the permissions are coarsely-grained.…”

Section: Methodsmentioning

confidence: 99%

“…Authors have developed models of privacy invasiveness by apps and their detection at run time as well as before installation in [24] and [25]. Thus, it is assumed that only the apps from non-malicious publishers have been installed.…”

Section: Threat Modelmentioning

confidence: 99%

“…iShield is a privacy preserving framework means for use in apps that pass the static and dynamic privacy breach analysis suggested in [24] and [25]. It can be used for balancing app functionalities and user's privacy.…”

Section: Threat Modelmentioning

confidence: 99%

“…as well as device related information such as name of Wi-Fi, MAC address, device version, model number etc. [24]. Thus, in this phase several privacy preserving techniques have been proposed to protect entire data of the users that is shared across the network.…”

Section: Permission Induced Risks For Mobile Usersmentioning

confidence: 99%

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iShield: A Framework for Preserving Privacyof iOS App User

Bhatt

Gupta

Mittal

2019

JCSM

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Do iOS apps honour user's privacy? Protection of user's privacy by apps has lately emerged as a big challenge. Many studies have identified that there exists an inherent trade-off between end user's privacy and apps'functionality. Some methods have been proposed to preserve user's privacy of specific data like location and health information. However, a comprehensive framework to enable privacy preserving data sharing by apps has not been found. In this paper, we have proposed iShield-a privacy preserving framework that can be easily integrated by developers at the time of app creation to enforce privacy with minimal performance overhead. Privacy threat to a user has been quantified by calculation of privacy disclosure score of an app user. Empirical results demonstrate that the approach significantly reduces the privacy disclosure of the user.

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Section: Ishield: Privacy Score Modelmentioning

confidence: 93%

Section: Methodsmentioning

confidence: 99%