Real-time privacy vulnerability detection techniques in software development: A Systematic Literature Review

Abstract

Real-time privacy vulnerability detection is one of the major concerns nowadays in developing secure software systems due to the growing complexity of software development and the increased attention to data privacy. This study conducts a Systematic Literature Review (SLR) to explore existing techniques, tools, and frameworks for detecting privacy vulnerabilities in real-time during the software development. We analyze relevant studies to identify key approaches, their effectiveness, and limitations by using the Kitchenham methodology and include it into the PRISMA framework. In addition, we categorize existing approaches into IDE integrated tools, network security solutions, mobile specific techniques, and general analysis tools. Summary tables further synthesize these techniques, tools, and their comparative attributes. Our findings reveal a variety of methods, including static and dynamic analysis, machine learning based detection, and integration of privacy-by-design. We also highlight challenges such as scalability, false positives, and the need for developer friendly tools. This review provides a comprehensive overview of the state-of-the-art in real-time privacy vulnerability detection approaches and offers insights into future research directions to enhance privacy protection in software development environments.