In the research of privacy preserving transaction data publishing, the existing methods are always designed for the centralized structure. The paper proposes a differential privacy publishing strategy to protect data privacy and maximize utility of the output data in the distributed environment. The new method combines the utility optimization of the output with differential privacy constraints and builds a distributed nonlinear programming model. Furthermore, two solutions based on global and local data respectively are designed to solve the distributed model securely. As shown in the theoretical analysis and the experimental results, the publishing strategy can achieve significant improvements in terms of privacy, security, and applicability.