A Privacy Preservation Quality of Service (QoS) Model for Data Exposure in Android Smartphone Usage

An Android smartphone contains built-in and externally downloaded applications that are used for entertainment, finance, navigation, communication, health and fitness, and so on. The behaviour of granting permissions requested by apps might expose the Android smartphone user to privacy risks. The ex...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Anizah Abu Bakar [verfasserIn] Manmeet Mahinderjit Singh [verfasserIn] Azizul Rahman Mohd Shariff [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	smartphone sensors mobile applications permissions risk quantification privacy exposure mathematical modelling

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 21(2021), 5, p 1667
Übergeordnetes Werk:	volume:21 ; year:2021 ; number:5, p 1667

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s21051667

Katalog-ID:	DOAJ085568473

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allfieldsSound	10.3390/s21051667 doi (DE-627)DOAJ085568473 (DE-599)DOAJ8efdf5a78f3c43648a37aa27fa760880 DE-627 ger DE-627 rakwb eng TP1-1185 Anizah Abu Bakar verfasserin aut A Privacy Preservation Quality of Service (QoS) Model for Data Exposure in Android Smartphone Usage 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier An Android smartphone contains built-in and externally downloaded applications that are used for entertainment, finance, navigation, communication, health and fitness, and so on. The behaviour of granting permissions requested by apps might expose the Android smartphone user to privacy risks. The existing works lack a formalized mathematical model that can quantify user and system applications risks. No multifaceted data collector tool can also be used to monitor the collection of user data and the risk posed by each application. A benchmark of the risk level that alerts the user and distinguishes between acceptable and unacceptable risk levels in Android smartphone user does not exist. Hence, to address privacy risk, a formalized privacy model called PRiMo that uses a tree structure and calculus knowledge is proposed. An App-sensor Mobile Data Collector (AMoDaC) is developed and implemented in real life to analyse user data accessed by mobile applications through the permissions granted and the risks involved. A benchmark is proposed by comparing the proposed PRiMo outcome with the existing available testing metrics. The results show that Tools & Utility/Productivity applications posed the highest risk as compared to other categories of applications. Furthermore, 29 users faced low and acceptable risk, while two users faced medium risk. According to the benchmark proposed, users who faced risks below 25% are considered as safe. The effectiveness and accuracy of the proposed work is 96.8%. smartphone sensors mobile applications permissions risk quantification privacy exposure mathematical modelling Chemical technology Manmeet Mahinderjit Singh verfasserin aut Azizul Rahman Mohd Shariff verfasserin aut In Sensors MDPI AG, 2003 21(2021), 5, p 1667 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:21 year:2021 number:5, p 1667 https://doi.org/10.3390/s21051667 kostenfrei https://doaj.org/article/8efdf5a78f3c43648a37aa27fa760880 kostenfrei https://www.mdpi.com/1424-8220/21/5/1667 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2021 5, p 1667
language	English
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callnumber-first	T - Technology
author	Anizah Abu Bakar
spellingShingle	Anizah Abu Bakar misc TP1-1185 misc smartphone sensors misc mobile applications misc permissions misc risk quantification misc privacy exposure misc mathematical modelling misc Chemical technology A Privacy Preservation Quality of Service (QoS) Model for Data Exposure in Android Smartphone Usage
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topic_title	TP1-1185 A Privacy Preservation Quality of Service (QoS) Model for Data Exposure in Android Smartphone Usage smartphone sensors mobile applications permissions risk quantification privacy exposure mathematical modelling
topic	misc TP1-1185 misc smartphone sensors misc mobile applications misc permissions misc risk quantification misc privacy exposure misc mathematical modelling misc Chemical technology
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title_auth	A Privacy Preservation Quality of Service (QoS) Model for Data Exposure in Android Smartphone Usage
abstract	An Android smartphone contains built-in and externally downloaded applications that are used for entertainment, finance, navigation, communication, health and fitness, and so on. The behaviour of granting permissions requested by apps might expose the Android smartphone user to privacy risks. The existing works lack a formalized mathematical model that can quantify user and system applications risks. No multifaceted data collector tool can also be used to monitor the collection of user data and the risk posed by each application. A benchmark of the risk level that alerts the user and distinguishes between acceptable and unacceptable risk levels in Android smartphone user does not exist. Hence, to address privacy risk, a formalized privacy model called PRiMo that uses a tree structure and calculus knowledge is proposed. An App-sensor Mobile Data Collector (AMoDaC) is developed and implemented in real life to analyse user data accessed by mobile applications through the permissions granted and the risks involved. A benchmark is proposed by comparing the proposed PRiMo outcome with the existing available testing metrics. The results show that Tools & Utility/Productivity applications posed the highest risk as compared to other categories of applications. Furthermore, 29 users faced low and acceptable risk, while two users faced medium risk. According to the benchmark proposed, users who faced risks below 25% are considered as safe. The effectiveness and accuracy of the proposed work is 96.8%.
abstractGer	An Android smartphone contains built-in and externally downloaded applications that are used for entertainment, finance, navigation, communication, health and fitness, and so on. The behaviour of granting permissions requested by apps might expose the Android smartphone user to privacy risks. The existing works lack a formalized mathematical model that can quantify user and system applications risks. No multifaceted data collector tool can also be used to monitor the collection of user data and the risk posed by each application. A benchmark of the risk level that alerts the user and distinguishes between acceptable and unacceptable risk levels in Android smartphone user does not exist. Hence, to address privacy risk, a formalized privacy model called PRiMo that uses a tree structure and calculus knowledge is proposed. An App-sensor Mobile Data Collector (AMoDaC) is developed and implemented in real life to analyse user data accessed by mobile applications through the permissions granted and the risks involved. A benchmark is proposed by comparing the proposed PRiMo outcome with the existing available testing metrics. The results show that Tools & Utility/Productivity applications posed the highest risk as compared to other categories of applications. Furthermore, 29 users faced low and acceptable risk, while two users faced medium risk. According to the benchmark proposed, users who faced risks below 25% are considered as safe. The effectiveness and accuracy of the proposed work is 96.8%.
abstract_unstemmed	An Android smartphone contains built-in and externally downloaded applications that are used for entertainment, finance, navigation, communication, health and fitness, and so on. The behaviour of granting permissions requested by apps might expose the Android smartphone user to privacy risks. The existing works lack a formalized mathematical model that can quantify user and system applications risks. No multifaceted data collector tool can also be used to monitor the collection of user data and the risk posed by each application. A benchmark of the risk level that alerts the user and distinguishes between acceptable and unacceptable risk levels in Android smartphone user does not exist. Hence, to address privacy risk, a formalized privacy model called PRiMo that uses a tree structure and calculus knowledge is proposed. An App-sensor Mobile Data Collector (AMoDaC) is developed and implemented in real life to analyse user data accessed by mobile applications through the permissions granted and the risks involved. A benchmark is proposed by comparing the proposed PRiMo outcome with the existing available testing metrics. The results show that Tools & Utility/Productivity applications posed the highest risk as compared to other categories of applications. Furthermore, 29 users faced low and acceptable risk, while two users faced medium risk. According to the benchmark proposed, users who faced risks below 25% are considered as safe. The effectiveness and accuracy of the proposed work is 96.8%.
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