To avoid the complex structure of deep learning models and significant training costs associated with them, the Broad Learning System (BLS) based on Random Vector Functional Link Neural Network was developed. BLS only has one input layer and one output layer and uses ridge regression theory to approximate the pseudoinverse of input, greatly simplifying the network and reducing training expenses. Despite its benefits, an attacker may use membership inference attacks to determine if some sample belongs to the model's training data when model parameters are revealed, leading to information leakage. Till now there is no related work on protecting BLS against membership inference attacks. To overcome this privacy issue we present a Privacy-Preserving Broad Learning System (PPBLS), by perturbing the objective function based on Functional Mechanism (FM) in differential privacy. We theoretically prove that PPBLS can satisfy E-differential privacy, and also demonstrate its effectiveness on both regression and classification tasks.