Focusing on the attack of frequent location as background knowledge causing user identity disclosure in geosocial network, a privacy-preserving algorithm based on frequent location was proposed. Firstly, The frequent location set was generated by the frequency of user check-in which was allocated for every user. Secondly,according to the background knowledge, hyperedges were composed by frequent location subset. Some hyperedges were remerged which did not meet anonymity parameter k, meanwhile the minimum bias of user and bias of location were chosen as hyperedges remerging metrics. Finally, in the comparison experiments with ( k,m)-anonymity algorithm, when the background knowledge was 3, the average bias of user and bias of location were decreased by about 19.1% and 8.3% on dataset Gowalla respectively, and about 22.2% and 10.7% on dataset Brightkite respectively. Therefore, the proposed algorithm can effectively preserve frequent location privacy, and reduces bias of user and location.

To solve the problems of needing labeling a great number of training data and pre-defining relation types in relation extraction methods based on supervised learning, a method for personal relation extraction by constructing the correlation network based on word co-occurrence information and performing graph clustering analysis on the correlation network was proposed. Firstly, 500 highly related person pairs for the research of relation extraction were gotten from the news title data. Secondly, the news data which contained related person pairs were crawled and performed pre-processing, and the keywords in the sentences which contained person pairs were gotten by the Term Frequency-Inverse Document Frequency (TF-IDF). Thirdly, the correlation between the words was acquired by the words co-occurrence information, and the key-words correlation network was constructed. Finally, the personal relations were acquired by the graph clustering analysis on the correlation network. In the relation extraction experiments, compared with the traditional algorithm of Chinese relation extraction based on word co-occurrence and pattern matching technology, the precision, recall and F-score of the proposed method were improved by 5.5, 3.7 and 4.4 percentage points respectively. The experimental results show that the proposed algorithm can effectively extract abundant and high-quality personal relation data from news data without labeling training data.