Collaborative edge data analytics is gaining prominence as vast amounts of data are increasingly generated at the network edge by smartphones, sensors, and smart cameras. In this work, we focus on the challenge of privacy-preserving video analytics on edge devices. Video data presents unique difficulties compared to other data types, primarily due to its large size and the nature of video analysis tasks. We evaluate and compare two leading techniques for enabling secure collaborative video analytics in distributed edge environments: Secure Multiparty Computation (MPC) and Trusted Execution Environments (TEE). To assess their effectiveness, we implement five real-world case studies, including object re-identification, scene similarity detection, vehicle counting, and machine learning–based video fusion tasks. Additionally, we benchmark fundamental image processing operations under various MPC configurations to identify the most efficient MPC protocols for video workloads. Our results show that while TEE offer significant performance benefits, especially for machine learning intensive tasks, MPC remains a practical alternative in scenarios without trusted hardware, particularly when using optimized secret sharing based 3-party protocols. We provide a comprehensive analysis of performance, security, and implementation complexity for both approaches.