Abstract
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.
Highlights
With the rapid development in the field of artificial intelligence and big data technology, data mining and data analysis have become an important tool for researchers to extract useful knowledge from data
With the increasing popularity of personal location information, people are increasingly using the system of recording and processing location data, which is usually called “location-based system.”. These systems include (a) location-based services (LBSs), in which a user obtains, typically in real-time, a service related to his current location, and (b) location data mining algorithms, used to determine points of interest and traffic patterns
In order to solve these problems, this paper proposes a frequent subgraph mining algorithm based on edge weight
Summary
With the rapid development in the field of artificial intelligence and big data technology, data mining and data analysis have become an important tool for researchers to extract useful knowledge from data. As an important part of mobile Internet services, location data mining and analysis have brought unprecedented changes and convenience to people’s work and life. With the increasing popularity of personal location information, people are increasingly using the system of recording and processing location data, which is usually called “location-based system.”. These systems include (a) location-based services (LBSs), in which a user obtains, typically in real-time, a service related to his current location, and (b) location data mining algorithms, used to determine points of interest and traffic patterns. Most mobile Internet services are based on the combination of location data with social data (personal information, social relationship information, etc.), and the crossduplication of these data will lead to privacy leakage problem [2]. PPDM encrypts and sanitizes sensitive information; if the location data is “excessively” protected, it is difficult for users to obtain relatively accurate services [3, 4]
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