<p>With the rapid advancement in information technology, data analysis has become increasingly vital in various fields. Balancing the utility of data while protecting individual privacy has become a hot topic for both academic research and practical applications. As a technology that can provide strict privacy guarantees, differential privacy has attracted widespread attention in recent years. In this paper, we study statistical inference for differentially private data based on empirical likelihood. Specifically, we develop two novel privacy-preserving-based statistical inference methods, including differentially private distributed empirical likelihood and balanced augmented differentially private distributed empirical likelihood. Under some mild conditions, the asymptotic properties of the proposed methods are derived. We also illustrate the finite sample performance of the proposed approaches via simulation studies and real data analysis.</p>