Publishing datasets plays an essential role in open data research and promoting the transparency of government agencies. However, such data publication might reveal users' private information. One of the most sensitive sources of data is geo-life datasets. Unfortunately, merely removing unique identifiers cannot preserve the privacy of users. Adversaries may know parts of the trajectories or be able to link the published dataset to other sources for the purpose of user identification. Therefore, it is crucial to apply privacy-preserving techniques before the publication of geo-life datasets. In this project, the proposed method is the MLA an enhanced anonymity framework termed k-anonymity algorithms to preserve the privacy of users in the publication of geo life datasets. MLA consists of 2 interworking algorithms clustering and alignment. Alignment: By formulating the anonymity process as an optimization problem and finding an alternative representation of the system. The clustering is able to apply machine clustering algorithms for clustering trajectories. The general approach is to implement one of the k-anonymity techniques and evaluate the privacy of the users to be compromised.