In the era of big data, the widespread adoption of Internet applications and information services has resulted in the extensive collection of individuals' sensitive biological information, increasing the risk of privacy breaches. Event cameras, as novel bio-inspired sensors, exhibit characteristics such as low latency, high dynamics, and texture independence. They offer a fresh technological approach to addressing privacy protection issues on the data side, making them suitable for private applications like home monitoring. This study thoroughly analyzes the research background of using event cameras for privacy protection, focusing on the privacy leakage issues of the big data era and the advantages of event cameras in privacy protection. It systematically reviews traditional methods for protecting sensitive bioinformation privacy, including face-template-based privacy protection, de-identification-based privacy protection, and privacy protection based on point cloud chaotic encryption. Additionally, it has examined the research progress in privacy-preserving event perception methods, including pedestrian re-identification, gesture recognition, and facial analysis. Further, the study also summarizes advancements in event-based image reconstruction and restoration, including intensity image reconstruction, image restoration, and video reconstruction, based on six algorithms. The results demonstrated that existing reconstruction algorithms have limited capability in recovering texture information, and the feasibility of the privacy protection technology based on event cameras is confirmed. For the future scaled-up application of event cameras, development recommendations are proposed, including reducing hardware costs, improving algorithm networks, and driving initiatives from a market perspective, aiming to provide a foundational reference for the deepened application of privacy protection using event cameras.