Dr. Arpita Jadhav Bhatt - ASSISTANT PROFESSOR (SENIOR GRADE) | JIIT Noida

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Dr. Arpita Jadhav Bhatt

Biography

Arpita Jadhav Bhatt is an Assistant Professor, Senior Grade, in the Department of Computer Science & Engineering and Information Technology, Jaypee Institute of Information and Technology, Noida, India. She obtained her PhD degree in Computer Science from Jaypee Institute of Information and Technology, Noida in the field of Information Privacy. She obtained her Masters in Engineering in Software Systems from Birla Institute of Technology and Science, Pilani (BITS Pilani) in 2010 and Bachelor of Technology degree from Rishiraj Institute of Technology, Indore in 2008. She has more than 13 years of experience in academics & industry and taught subjects like Mobile Computing, Mobile Application Engineering, Web Technology, Programming in iOS & Android, Open Source Software Development, Data Structures, Software Developmental Fundaments (I and II). She has guided various graduate projects in various fields namely mobile app development for Android/iOS, information security, machine learning, web app development, etc. She has been actively participating in various conferences, faculty development programs, and workshops. She has published various articles in reputed SCI/Scopus journals, conferences and book chapters. She is a reviewer of various international journals and conferences. Her areas of interest are Machine Learning, Mobile Business Intelligence, Mobile Application Engineering, Information Privacy, Software Engineering, Programming in Android, Mobile Computing, reverse engineering of mobile apps.

Research Highlights

My research focuses on strengthening information privacy for mobile users, particularly within the iOS, where I have worked extensively on the detection of malicious and privacy-infringing apps using a combination of static and dynamic analysis, machine learning, and decision-making techniques. My key contributions include the use of hybrid analysis methods and active learning to improve privacy leak detection in iOS apps. I have also explored the computation of Privacy Disclosure Scores using supervised classifiers to assess the privacy risks posed by mobile apps. My work on permission-based analysis supports category-wise classification of apps to predict behavior patterns and potential threats. My recent research work utilizes multi-criteria decision-making approach to enhance the accuracy of malicious app detection by evaluating multiple risk factors. Additional areas of study include comparison of mobile security tools, analysis at the binary level for iOS apps, and user privacy preservation techniques. Collectively, my research aims to improve the privacy, security, and trustworthiness of mobile apps through intelligent and scalable analysis methods.

Areas of Interest

Information Privacy in Mobile Applications
Detection of Malicious iOS and Android Apps
Machine Learning for Privacy and Security
Mobile App Behavior Analysis (Static & Dynamic)
IoT Malware Detection and Network Forensics

Publications

Bhatt AJ, Sardana N. Malicious iOS apps detection through Multi-Criteria Decision-Making Approach. Informatica. 2025 Mar 10;49(1).
Arpita JB, Neetu S. Detection of IoT Malware using Network Forensics and Modeling. International Journal of Performability Engineering. 2024 Oct 29;20(10):621.
Bhatt AJ, Gupta C, Mittal S. iABC-AL: Active learning-based privacy leaks threat detection for iOS applications. Journal of King Saud University-Computer and Information Sciences. 2021 Sep 1;33(7):769-86.
Bhatt AJ, Gupta C, Mittal S. iShield: A Framework for Preserving Privacy of iOS App User. Journal of Cyber Security and Mobility. 2019 Jan 11:493-536.
Bhatt AJ, Gupta C, Mittal S. iABC: Towards a hybrid framework for analyzing and classifying behaviour of iOS applications using static and dynamic analysis. Journal of Information Security and Applications. 2018 Aug 1;41:144-58.