Abstract: Association rule mining is an important task in data mining. To solve the problem of privacy in the process of mining, especially under the given level of privacy protection, how to improve the usability of mining results, this paper proposes an association rule mining algorithm that satisfies differential privacy(TS-ARM) and designs a new transaction segmentation method to reduce the dimensionality of the data set. A dual conditional mechanism is used to transactions that exceed the length threshold, that is, the segmentation condition meets both the segmentation length threshold and the segmentation support threshold at the same time. Compared with the traditional transaction truncation method, this transaction segmentation method can effectively control the information loss caused by the addition of noise while reducing the sensitivity of the query and reducing the required noise scale. Theoretical analysis and experiments prove that the proposed algorithm not only guarantees privacy, but also improves the availability of data mining results under a given security level.