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Adaptive Differential Privacy for Protecting User Confidential Information on Android Devices | ||
| Journal of Information Technology Management | ||
| دوره 17، Special Issue on SI: Intelligent Security and Management، 2025، صفحه 155-167 اصل مقاله (1.18 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/jitm.2025.102933 | ||
| نویسندگان | ||
| Manish Verma* 1؛ Parma Nand2 | ||
| 1PhD Scholar, Department of CSE, SSET, Sharda University, Greater Noida, U.P. India. | ||
| 2Prof., Department of CSE, SSET, Sharda University, Greater Noida, U.P., India. | ||
| چکیده | ||
| The widespread adoption of Android phones has heightened concerns about user privacy. This research presents an Adaptive Privacy Management System (APMS) that integrates Machine Learning (ML) models with Differential Privacy techniques to enhance privacy protection. The APMS monitors application behavior and employs ML algorithms to detect anomalies and enable context-aware privacy enforcement. Differential Privacy ensures that sensitive data remains protected through the addition of noise and privacy-preserving computations. Experimental results demonstrate that the APMS achieves a 92.5% accuracy rate in detecting the privacy leakage. The anomaly detection model, using Random Forest, shows high accuracy (92.5%), recall (89.5%), and precision (73.9%), effectively identifying both normal and anomalous behaviors. Additionally, the impact of noise on data utility, controlled by the privacy budget (ε), is manageable. The results show that APMS is a robust system for safeguarding user confidential information, contributing to a more secure and privacy-centric Android ecosystem. | ||
| کلیدواژهها | ||
| Android Security؛ Data Protection؛ Confidential Information؛ Data Leakage؛ App Vul-nerabilities | ||
| مراجع | ||
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Abdella, J., Ozuysal, M., & Tomur, E. (2016). CA-ARBAC: Privacy preserving using context-aware role based access control on Android permission system. Networks, 00, 1–23. https://doi.org/10.1002/sec
Alkindi, Z. R., Sarrab, M., & Alzeidi, N. (2021). User privacy and data flow control for Android apps: Systematic literature review. Journal of Cyber Security and Mobility, 10(1), 261–304. https://doi.org/10.13052/jcsm2245-1439.1019
Alsoubai, A., Ghaiumy Anaraky, R., Li, Y., Page, X., Knijnenburg, B., & Wisniewski, P. J. (2022, April 29). Permission vs. app limiters: Profiling smartphone users to understand differing strategies for mobile privacy management. Conference on Human Factors in Computing Systems - Proceedings. https://doi.org/10.1145/3491102.3517652
Arzt, S., Rasthofer, S., Fritz, C., Bodden, E., Bartel, A., Klein, J., Le Traon, Y., Octeau, D., & McDaniel, P. (2014). FLOWDROID: Precise context, flow, field, object-sensitive and lifecycle-aware taint analysis for Android apps. ACM SIGPLAN Notices, 49(6), 259–269. https://doi.org/10.1145/2594291.2594299
Ashisha, G. R., Mary, A. X., George, T. S., Sagayam, M. K., Fernandez-Gamiz, U., Günerhan, H., Uddin, M. N., & Pramanik, S. (2023). Analysis of diabetes disease using machine learning techniques: A review. Journal of Information Technology Management, 15(4), 139–159. University of Tehran. https://doi.org/10.22059/jitm.2023.94897
Bou Chaaya, K. (2021). Privacy management in connected environments (Doctoral dissertation, Université de Pau et des Pays de l'Adour).
Cholevas, C., Angeli, E., Sereti, Z., Mavrikos, E., & Tsekouras, G. E. (2024). Anomaly detection in blockchain networks using unsupervised learning: A survey. Algorithms, 17(5). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/a17050201
Demontis, A., Melis, M., Biggio, B., Maiorca, D., Arp, D., Rieck, K., Corona, I., Giacinto, G., & Roli, F. (2017). Yes, machine learning can be more secure! A case study on Android malware detection. http://arxiv.org/abs/1704.08996
Duan, M., Jiang, L., Shar, L. K., & Gao, D. (2022). UIPDroid. 227–231. https://doi.org/10.1145/3510454.3516844
Enck, W., Gilbert, P., Han, S., Tendulkar, V., Chun, B. G., Cox, L. P., Jung, J., McDaniel, P., & Sheth, A. N. (2014). TaintDroid: An information-flow tracking system for realtime privacy monitoring on smartphones. ACM Transactions on Computer Systems, 32(2). https://doi.org/10.1145/2619091
Fawaz, K., & Shin, K. G. (2014). Location privacy protection for smartphone users. Proceedings of the ACM Conference on Computer and Communications Security, 239–250. https://doi.org/10.1145/2660267.2660270
Harikrishnan, P. R., & Periyasamy, P. (2024, August). A review on the analysis of the effectiveness of permission-based security models in Android apps. In 2024 7th International Conference on Circuit Power and Computing Technologies (ICCPCT) (Vol. 1, pp. 1451–1459). IEEE.
Kyritsis, A. (2019). Archive ouverte UNIGE: Enhancing wellbeing using artificial intelligence techniques. https://doi.org/10.13097/archive-ouverte/unige:130751
Mishra, B., Agarwal, A., Goel, A., Ansari, A. A., Gaur, P., Singh, D., & Lee, H.-N. (2022). Privacy protection framework for Android. IEEE Access, 10, 7973–7988. https://doi.org/10.1109/ACCESS.2022.3142345
Niu, B., Li, Q., Wang, H., Cao, G., Li, F., & Li, H. (2022). A framework for personalized location privacy. IEEE Transactions on Mobile Computing, 21(9), 3071–3083. https://doi.org/10.1109/TMC.2021.3055865
Pattun, G., Afroaz, K., Siddiqui, A. T., & Ghazala, S. (2023). Prediction of type-I and type-II diabetes: A hybrid approach using fuzzy logic and machine learning algorithms. Journal of Information Technology Management, 15, 35–56. https://doi.org/10.22059/jitm.2023.95244
Rahman, M. R., Miller, E., Hossain, M., & Ali-Gombe, A. (2022). Intent-aware permission architecture: A model for rethinking informed consent for Android apps. arXiv preprint arXiv:2202.06995.
Scoccia, G. L., Ruberto, S., Malavolta, I., Autili, M., & Inverardi, P. (2018). An investigation into Android run-time permissions from the end users’ perspective. Proceedings of the International Conference on Software Engineering, 45–55. https://doi.org/10.1145/3197231.3197236
Tahaei, M., Abu-Salma, R., & Rashid, A. (2023, April 19). Stuck in the permissions with you: Developer & end-user perspectives on app permissions & their privacy ramifications. Conference on Human Factors in Computing Systems - Proceedings. https://doi.org/10.1145/3544548.3581060
Thangamayan, S., Sinha, A., Moyal, V., Maheswari, K., Harathi, N., & Utama, A. N. B. (2024). Comparative study on different machine learning algorithms for neonatal diabetes detection. Journal of Information Technology Management, 16(1), 5–26. https://doi.org/10.22059/jitm.2024.96359
Verma, M., & Nand, P. (2023). Review on the static analysis techniques used for privacy leakage detection in Android apps. In B. Unhelkar, H. M. Pandey, A. P. Agrawal, & A. Choudhary (Eds.), Advances and applications of artificial intelligence & machine learning. ICAAAIML 2022 (Lecture Notes in Electrical Engineering, Vol. 1078). Springer, Singapore. https://doi.org/10.1007/978-981-99-5974-7_28
Wu, F., Sun, R., Fan, W., Liu, Y., Liu, F., & Lu, H. (2018). A privacy protection approach based on Android application’s runtime behavior monitor and control. International Journal of Digital Crime and Forensics, 10(3), 95–113. https://doi.org/10.4018/IJDCF.2018070108
Xu, R., Baracaldo, N., & Joshi, J. (2021). Privacy-preserving machine learning: Methods, challenges and directions. http://arxiv.org/abs/2108.04417
Yan, C., Meng, M. H., Xie, F., & Bai, G. (2024). Investigating documented privacy changes in Android OS. Proceedings of the ACM on Software Engineering, 1(FSE), 2701–2724. https://doi.org/10.1145/3660826
Zhang, S., Lei, H., Wang, Y., Li, D., Guo, Y., & Chen, X. (2023). How Android apps break the data minimization principle: An empirical study. Proceedings of the 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE 2023), 1238–1250. https://doi.org/10.1109/ASE56229.2023.00141
Zhang, X., Yadollahi, M. M., Dadkhah, S., Isah, H., Le, D. P., & Ghorbani, A. A. (2022). Data breach: Analysis, countermeasures and challenges. International Journal of Information and Computer Security, 19(3–4), 402–442. https://doi.org/10.1504/ijics.2022.127169 | ||
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