Clustering algorithm is used to preprocess personal privacy information in order to achieve differential privacy protection, which can reduce the reconstruction error caused by directly distributing histogram data, and the reconstruction error caused by different combining methods of histogram. Aiming at the problem of sensitivity to input data parameters in DP-DBSCAN (Differential Privacy-Density-Based Spatial Clustering of Applications with Noise) differential privacy algorithm, the OPTICS (Ordering Points To Identify Clustering Structure) algorithm based on density clustering was applied to differential privacy protection. And an improved differential privacy protection algorithm, called DP-OPTICS (Differential Privacy-Ordering Points To Identify Clustering Structure) was introduced, the sparse dataset was compressed, the same variance noise and different variance noise were used as two noise-adding ways by comparison, considering the probability of privacy information's being broken by the attacker, the upper bound of privacy parameter ε was determined, which effectively balanced the relationship between the privacy of sensitive information and the usability of data. The DP-OPTICS algorithm was compared with the differential privacy protection algorithm based on OPTICS clustering and DP-DBSCAN algorithm. The DP-OPTICS algorithm is between the other two in time consumption. However, in the case of having the same parameters, the stability of the DP-OPTICS algorithm is the best among them, so the improved OP-OPTICS differential privacy protection algorithm is generally feasible.