Introduction: As smart cities continue to evolve, the integration of advanced technologies and data analytics plays a pivotal role in optimizing urban services. However, the increasing reliance on data raises concerns about privacy and security. This research addresses the critical need for privacy-preserving data analytics in smart cities to balance the benefits of data-driven decision-making with the protection of individuals' privacy. Problem Statement: Smart cities generate vast amounts of data from diverse sources, including sensors, IoT devices, and social media. The unregulated use of this data poses significant threats to the privacy of residents. Traditional data analytics methods may compromise sensitive information, necessitating the development of privacy-preserving approaches to ensure the responsible use of urban data. Objective: This research aims to design and implement privacy-preserving data analytics techniques tailored for smart cities. The objective is to enable efficient data analysis while safeguarding the privacy of individuals. By employing advanced cryptographic and anonymization methods, the research seeks to strike a balance between the utility of data and the protection of personal information. Methodology: The research methodology involves a comprehensive review of existing privacy-preserving techniques and their applicability to smart city environments. Subsequently, a novel framework will be developed, integrating cryptographic protocols, anonymization algorithms, and other privacy-enhancing measures. The framework will be evaluated using real-world smart city datasets to assess its effectiveness in preserving privacy while maintaining the utility of the analyzed data. Results: The results will include an in-depth analysis of the proposed privacy-preserving data analytics framework, comparing its performance with traditional methods. Evaluation metrics will focus on the accuracy of analytics, computational efficiency, and the level of privacy protection achieved. The findings aim to provide insights into the feasibility and effectiveness of adopting privacy-preserving measures in smart city data analytics. Conclusion: This research contributes to the emerging field of privacy-preserving data analytics in smart cities by proposing a novel framework that balances the benefits of data-driven decision-making with the protection of individual privacy. The findings highlight the importance of incorporating privacy-enhancing measures into smart city infrastructures to ensure responsible and ethical data use.