Insufficient authorization and overauthorization are two main problems to be solved in access control systems. If the authorization is too strict, users might not be able to access data that should be accessible. If the authorization is too lax, users might obtain too many access rights, which may cause considerable risks. Finer-grained access control models are needed to solve these problems. In this paper, aiming at insufficient authorization in text databases, we propose the topic-based access control (TBAC) model and two implementation methods of the model (subject-to-object topic-based access control method PD-TBAC and object-to-subject topic-based access control method FD-TBAC). In the TBAC model, the access control decision for each user against each file is totally content-driven. We use the latent Dirichlet allocation (LDA) algorithm to extract topics from each paragraph in each file, and these topics are used to determine users’ access rights. Experimental results show that the access control granularity of TBAC is more than 4 times that of the existing content-based access control model.

Now, many application services based on location data have brought a lot of convenience to people’s daily life. However, publishing location data may divulge individual sensitive information. Because the location records about location data may be discrete in the database, some existing privacy protection schemes are difficult to protect location data in data mining. In this paper, we propose a travel trajectory data record privacy protection scheme (TMDP) based on differential privacy mechanism, which employs the structure of a trajectory graph model on location database and frequent subgraph mining based on weighted graph. Time series is introduced into the location data; the weighted trajectory model is designed to obtain the travel trajectory graph database. We upgrade the mining of location data to the mining of frequent trajectory graphs, which can discover the relationship of location data from the database and protect location data mined. In particular, to improve the identification efficiency of frequent trajectory graphs, we design a weighted trajectory graph support calculation algorithm based on canonical code and subgraph structure. Moreover, to improve the data utility under the premise of protecting user privacy, we propose double processes of adding noises to the subgraph mining process by the Laplace mechanism and selecting final data by the exponential mechanism. Through formal privacy analysis, we prove that our TMDP framework satisfies ε‐differential privacy. Compared with the other schemes, the experiments show that the data availability of the proposed scheme is higher and the privacy protection of the scheme is effective.