Multi-issue negotiation protocols represent a promising field since most negotiation problems in the real world involve multiple issues. Our work focuses on negotiation with interdependent issues, in which agent utility functions are nonlinear. Existing works have not yet focused on agents’ private information. In addition, they were not scalable in the sense that they have shown a high failure rate for making agreements among 5 or more agents. In this paper, we focus on a novel multi-round representative-based protocol that utilizes the amount of agents’ private information revealed. Experimental results demonstrate that our mechanism reduces the failure rate in making agreements, and it is scalable on the number of agents compared with existing approaches. Categories and Subject Descriptors I.2.11 [Artificial Intelligence]: Distributed Artificial Intelligence - Multi-agent System General Terms Algorithms Keywords Multi-issue Negotiation, Nonlinear Function, Complex Utility