In order to achieve effective integration and accurate fusion of multi-source Point of Interest (POI) data, a Mutually-Nearest Method considering Distance and Category (MNMDC) was proposed. Firstly, for spatial attributes, standardized weight algorithm was used to calculate the spatial similarity of the object to be fused, and the fusion set was obtained. Secondly, for non-spatial attributes, Jaro-Winkle algorithm was used to eliminate some objects with consistent categories by a low threshold, and exclude some objects with inconsistent categories by a high threshold. Finally, non-spatial Jaro-Winkle algorithm with distance constraint, category consistency constraint and high threshold was used to find out the missing objects in the spatial algorithm. The experimental results show that the average accuracy reaches 93.3%, compared with Combined Normal Weight and Title-similatity algorithm (COM-NWT) and the grid correction methods, the accuracy of MNMDC method in seven different groups of coincidence degree data, the average accuracy increases by 2.7 percentage points and 1.6 percentage points, the average recall increases by 2.3 and 1.4 percentage points. The MNMDC method allows more accurate fusion of POI data during actual fusion.

In view of a large number of privacy disclosure issues when using k-anonymity method directly for multi-sensitive attribute data publishing, a joint privacy-sensitive properties preserving algorithm based on semantic similarity and multidimensional weighting was proposed. This algorithm realized security protection of the joint-sensitive property value and the semantic diversity of the privacy group with the help of the semantic similarity anti-clustering principle and counter-sensitive property value. According to different application needs, data privacy protection of different extent was provided. The experimental results show that this method can effectively protect data privacy and enhance data security and practicality.