Machine learning models trained by large volume of proprietary data and intensive computational resources are valuable assets of their owners, who merchandise these models to third-party users through prediction service API. However, existing literature shows that model parameters are vulnerable to extraction attacks which accumulate a large number of prediction queries and their responses to train a replica model. As countermeasures, researchers have proposed to reduce the rich API output, such as hiding the precise confidence level of the prediction response. Nonetheless, even with response being only one bit, an adversary can still exploit fine-tuned queries with differential property to infer the decision boundary of the underlying model. In this paper, we propose boundary differential privacy (ϵ -BDP) as a solution to protect against such attacks by obfuscating the prediction responses near the decision boundary. ϵ -BDP guarantees an adversary cannot learn the decision boundary by a predefined precision no matter how many queries are issued to the prediction API. We design and prove a perturbation algorithm called boundary randomized response that can achieve ϵ -BDP. The effectiveness and high utility of our solution against model extraction attacks are verified by extensive experiments on both linear and non-linear models.