Edge intelligence integrates edge computing and artificial intelligence for real-time data processing and decision-making at the data source, reducing latency and optimizing bandwidth for distributed IoT and smart device applications, empowering smarter, more efficient systems in real-world environments. However, this decentralized approach introduces significant privacy challenges, as sensitive information is processed across multiple, often resource-constrained, nodes. In this talk, we explore state-of-the-art privacy-preserving techniques within edge intelligence, with a focus on practical evaluations and real-world insights. We delve into methods such as differential privacy, secure multiparty computation, and homomorphic encryption, examining their effectiveness in safeguarding data while maintaining system performance. Through a series of empirical studies, we analyse the trade-offs between computational efficiency and privacy guarantees, highlighting the challenges and opportunities inherent in deploying these techniques in dynamic edge environments. Our findings underscore the need for a balanced approach that not only fortifies privacy but also supports the rapid processing demands of edge applications. Finally, we discuss emerging trends and propose a roadmap for future research to improve the performance of privacy preserving techniques with the operational constraints of edge intelligence. This talk will provide valuable perspectives for researchers and practitioners seeking to navigate the complexities of privacy in edge computing, ultimately fostering the development of more secure and efficient intelligent systems.