Recently, big data related to human movement, air quality, and meteorology have been generated in urban computing through sensing technology and the computing infrastructure. However, security problems arise as data utilization increases. If the sensing data from internet of things devices are constantly exposed, the users’ private information can be determined, a critical security risk that could result in privacy breaches. This paper proposes a secure data processing system using the blockchain and differential privacy for data security and privacy protection in urban computing. When a service provider requests information, the system generates it from urban computing data using machine learning. We apply differential privacy to these data to protect privacy. However, if a query repeats, differential privacy may provide insufficient privacy protection. Therefore, we reduce the total privacy cost by reusing noise for the same data and privacy parameters using the blockchain. Machine learning accuracy may decrease when noisy data are used for training. Thus, we increase accuracy by storing and appropriately using the model parameters generated by the same data in the blockchain. We design, simulate, and analyze the results of an experimental environment for reusing noise for differential privacy and parameter utilization of machine learning using the blockchain. The proposed approach reduces privacy costs compared to the existing mechanism while protecting data privacy. We demonstrate that, through parameter utilization, the accuracy improves compared to conventional mechanisms.