The rapid growth of urban populations has necessitated the adoption of smart technologies to enhance urban planning intelligence. Crowdsensing, which leverages data collected from large-scale user participation through mobile devices and IoT sensors, has emerged as a powerful tool for monitoring and optimising urban infrastructure, transportation, and public services. However, the integration of crowdsensing into urban management introduces significant security and privacy challenges such as data integrity, confidentiality, and user anonymity. This chapter explores the role of security in crowdsensing-based urban management applications and highlights key threats such as data falsification, adversarial attacks, and privacy breaches. Additionally, it discusses advanced security mechanisms, including encryption, differential privacy, Blockchain-based trust models, and secure multiparty computation, to ensure the reliability and confidentiality of crowdsensed data. By implementing robust security frameworks, urban management systems are able to utilise full potential of crowdsensing as well as safeguarding sensitive user data to ensure data authenticity and enhance trust among participants.