{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:50:29Z","timestamp":1760233829276,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,3,1]],"date-time":"2021-03-01T00:00:00Z","timestamp":1614556800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004595","name":"Universiti Sains Malaysia","doi-asserted-by":"publisher","award":["1001\/PKOMP\/8014003"],"award-info":[{"award-number":["1001\/PKOMP\/8014003"]}],"id":[{"id":"10.13039\/501100004595","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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 &amp; 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%.<\/jats:p>","DOI":"10.3390\/s21051667","type":"journal-article","created":{"date-parts":[[2021,3,1]],"date-time":"2021-03-01T01:26:33Z","timestamp":1614561993000},"page":"1667","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Privacy Preservation Quality of Service (QoS) Model for Data Exposure in Android Smartphone Usage"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4030-053X","authenticated-orcid":false,"given":"Anizah","family":"Abu Bakar","sequence":"first","affiliation":[{"name":"School of Computer Science, Universiti Sains Malaysia, Gelugor 11800, Penang, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8081-5223","authenticated-orcid":false,"given":"Manmeet","family":"Mahinderjit Singh","sequence":"additional","affiliation":[{"name":"School of Computer Science, Universiti Sains Malaysia, Gelugor 11800, Penang, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Azizul Rahman","family":"Mohd Shariff","sequence":"additional","affiliation":[{"name":"School of Computer Science, Universiti Sains Malaysia, Gelugor 11800, Penang, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Perwej, Y., Haq, K., Parwej, F., Mumdouh, M., and Hassan, M. (2019). The Internet of Things (IoT) and its Application Domains. Int. J. Comput. Appl.","DOI":"10.5120\/ijca2019918763"},{"key":"ref_2","unstructured":"(2020). Number of Smartphone Users Worldwide from 2016 to 2021, Statista."},{"key":"ref_3","unstructured":"Clement, J. (2021). Google Play Store: Number of apps 2019, Statista."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.ijinfomgt.2019.05.010","article-title":"Mobile users\u2019 information privacy concerns and the role of app permission requests","volume":"50","author":"Degirmenci","year":"2020","journal-title":"Int. J. Inf. Manag."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chen, T., Wu, F., Luo, T.T., Wang, M., and Ho, Q. (2016). Big Data Management and Analytics for Mobile Crowd Sensing. Mob. Inf. Syst., 1\u20132.","DOI":"10.1155\/2016\/8731802"},{"key":"ref_6","unstructured":"Atkinson, M. (2015). An Analysis of Apps in the Google Play Store, Pew Research Center."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1361","DOI":"10.1109\/JSYST.2014.2364202","article-title":"Leakage Detection and Risk Assessment on Privacy for Android Applications: LRPdroid","volume":"10","author":"Lo","year":"2016","journal-title":"IEEE Syst. J."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Alshehri, A., Marcinek, P., Alzahrani, A., Alshahrani, H., and Fu, H. (2019, January 6\u20138). Puredroid: Permission usage and risk estimation for android applications. Proceedings of the 3rd International Conference on Information System and Data Mining, Houston, TX, USA.","DOI":"10.1145\/3325917.3325941"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Sharif, K., and Tenbergen, B. (2020). Smart Home Voice Assistants: A Literature Survey of User Privacy and Security Vulnerabilities. Complex Syst. Inform. Model. Q., 15\u201330.","DOI":"10.7250\/csimq.2020-24.02"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Liu, K., and Terzi, E. (2009, January 6\u20139). A framework for computing the privacy scores of users in online social networks. Proceedings of the IEEE International Conference on Data Mining, Miami, FL, USA.","DOI":"10.1109\/ICDM.2009.21"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Chih-Chang, K., Nokhbeh, R., Barber, Z.K.S., and Chang, K.C. (2020). A Framework for Estimating Privacy Risk Scores of Mobile Apps, Springer.","DOI":"10.1007\/978-3-030-62974-8_13"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1007\/s11219-018-9418-6","article-title":"Automated functional testing of mobile applications: A systematic mapping study","volume":"27","author":"Tramontana","year":"2019","journal-title":"Softw. Qual. J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1016\/j.procs.2020.03.124","article-title":"What Do People Complain about Drone Apps? A Large-Scale Empirical Study of Google Play Store Reviews","volume":"170","author":"Kalaichelavan","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_14","unstructured":"(2020, November 04). Android Permissions on Android. Available online: https:\/\/developer.android.com\/guide\/topics\/permissions\/overview."},{"key":"ref_15","unstructured":"(2020, September 12). Samsung Search. Available online: https:\/\/www.samsung.com\/my\/search\/?searchvalue=samsung apps#."},{"key":"ref_16","unstructured":"(2020). ColorOS 7.1 User Guide, OPPO."},{"key":"ref_17","unstructured":"(2021, January 18). Huawei HUAWEI AppGallery. Available online: https:\/\/consumer.huawei.com\/en\/mobileservices\/appgallery\/."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1016\/j.procs.2015.07.223","article-title":"Survey on mobile user\u2019s data privacy threats and defense mechanisms","volume":"56","author":"Khan","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ferra, F., Wagner, I., Boiten, E., Hadlington, L., Psychoula, I., and Snape, R. (2020). Challenges in assessing privacy impact: Tales from the front lines. Secur. Priv., 3.","DOI":"10.1002\/spy2.101"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Lu, X., Qu, Z., Li, Q., and Hui, P. (2015). Privacy information security classification for internet of things based on internet data. Int. J. Distrib. Sens. Netw.","DOI":"10.1109\/IIKI.2014.40"},{"key":"ref_21","unstructured":"Owoh, N.P., and Mahinderjit Singh, M. (2018). Security analysis of mobile crowd sensing applications. Appl. Comput. Inform."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhu, K., He, X., Xiang, B., Zhang, L., and Pattavina, A. (2016). How dangerous are your Smartphones? App usage recommendation with privacy preserving. Mob. Inf. Syst., 2016.","DOI":"10.1155\/2016\/6804379"},{"key":"ref_23","first-page":"2848","article-title":"Data Privacy Quantification using Multifaceted App-Sensor Mobile Data Collector tool (AMoDaC) for Smartphone","volume":"63","year":"2020","journal-title":"Solid State Technol."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ogata, M., Franklin, J., Voas, J., Sritapan, V., and Quirolgico, S. (2019). Vetting the Security of Mobile Applications.","DOI":"10.6028\/NIST.SP.800-163r1"},{"key":"ref_25","unstructured":"Chen, S., Fan, L., Meng, G., Su, T., Xue, M., Xue, Y., Liu, Y., and Xu, L. (July, January 24). An Empirical Assessment of Security Risks of Global Android Banking Apps. Proceedings of the ACM\/IEEE 42nd International Conference on Software Engineering, Seoul, Korea."},{"key":"ref_26","unstructured":"He, W., Tian, X., and Shen, J. (2015, January 25\u201326). Examining Security Risks of Mobile Banking Applications through Blog Mining. Proceedings of the Modern Artificial Intelligence and Cognitive Science Conference, Greensboro, NC, USA."},{"key":"ref_27","unstructured":"Lau, B., Zhang, J., Bereford, A.R., Thomas, D., and Mayrhofer, R. (2020). Uraniborg\u2019s Device Preloaded App Risks Scoring Metrics, Institute of Networks and Security."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Jozani, M., Ayaburi, E., Ko, M., and Choo, K.K.R. (2020). Privacy concerns and benefits of engagement with social media-enabled apps: A privacy calculus perspective. Comput. Hum. Behav.","DOI":"10.1016\/j.chb.2020.106260"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1007\/s42979-020-00287-9","article-title":"Personal Information Disclosure via Voice Assistants: The Personalization\u2013Privacy Paradox","volume":"1","author":"Pal","year":"2020","journal-title":"SN Comput. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"106405","DOI":"10.1016\/j.chb.2020.106405","article-title":"South African millennials\u2019 acceptance and use of retail mobile banking apps: An integrated perspective","volume":"111","author":"Thusi","year":"2020","journal-title":"Comput. Hum. 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