QR-FLBS: A Personalized Location Big Data Privacy Protection Model

Abstract

With the rapid development of mobile communication and location-aware device technology, many smart mobile terminal data are generated daily. These data are not only large in quantity and value, but also based on location service, so it is called big location data based on location service. In recent years, large data generated by location-based services has become a research hot spot in the industry. At the same time, people pay more and more attention to the privacy protection of big data. Given the limitations of the traditional privacy protection model based on false location of big location data, this paper proposes a model QR-FLBS (Quick Response-Fingerprint Location-Based Services) based on fingerprint identification and two-dimensional code technology of smart terminals, which can better solve the privacy protection of location services to a certain extent. This experiment adopts the data from real data sets and compares it with the privacy-protection method of false location. The performance of the QR-FLBS model is measured by the response time, robustness and privacy. The experimental results show that the scheme is suitable for the privacy protection of location services and improves privacy protection without sacrificing the quality of user location services.