With the proliferation of IoT cameras, it is possible to use crowd-sourced videos to help find interested targets (e.g., crime suspect, lost child, lost vehicle, etc.) on demand. Due to the ubiquity of IoT cameras such as dash mounted cameras and phone camera, the crowdsourced videos have much better spatial coverage compared to only using surveillance cameras, and thus can significantly improve the effectiveness of target search. However, this may raise privacy concerns when workers (owners of IoT cameras) are provided with photos of the target. Also, the videos captured by the workers may be misused to track bystanders. To address this problem, we design and implement TargetFinder, a privacy preserving system for target search through IoT cameras. By exploiting homo-morphic encryption techniques, the server can search for the target on encrypted information. We also propose techniques to allow the requester (e.g., the police) to receive images that include the target, while all other captured images of the bystanders are not revealed. Moreover, the target's face image is not revealed to the server and the participating workers. Due to the high computation overhead of the cryptographic primitives, we develop optimization techniques in order to run our privacy preserving protocol on mobile devices. A real-world demo and extensive evaluations demonstrate the effectiveness of TargetFinder.