Privacy-Preserving Machine Learning Framework for Next-Generation Smart City Security

India's ambitious Smart Cities Mission is rapidly reshaping urban spaces into intricately linked, data-driven systems. This profound transformation, while delivering unprecedented efficiency and enhanced life, simultaneously reveals an innovative and complex set of security challenges that transcend traditional digital borders. This paper addresses the critical and inseparable contribution of machine learning (ML) in fortifying India's plural security environment of emerging smart cities. We start by highlighting the imperative of an intelligent security strategy in the Indian context. Based on a thorough review of real-world security challenges and analysis of six core research contributions, this paper advocates for a new, multi-layered machine learning framework for the construction of a secure, resilient, and privacy-sensitive urban future. The suggested framework encompasses edge computing, federated learning, and advanced deep learning models. We substantiate the effectiveness of this framework by describing its deployment in threat detection, predictive analysis, and robust privacy protection, all rooted in realistic examples and the unique socio-technical context of India.