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QR-FLSB: a Personalized Location Big Data Privacy Protection Model

EasyChair Preprint no. 7152

6 pagesDate: December 4, 2021


With the rapid development of mobile communication and location-aware device technology, a large number of mobile intelligent terminal data will be generated every day. These data are not only large in data quantity and value, but also based on location service, so it is called location big data based on location service. In recent years, large data generated by location-based services has become a research hotspot in the industry. At the same time, people pay more and more attention to the privacy protection of big data. In view of the limitations of traditional privacy protection model based on "false location" of big location data, this paper proposes a model QR-FLSB (Quick Response-Fingerprint Location Based Services) based on fingerprint identification and two-dimensional code technology of intelligent 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 location service privacy protection method of "false location". The performance of QR-FLSB model is measured by response time, robustness and privacy. The experimental results show that the scheme is not only suitable for the privacy protection of location services, but also improves the privacy protection of location services without sacrificing the quality of user location services.

Keyphrases: False position, fingerprint identification, K-anonymity technology, location privacy protection, location services, Two-dimensional code technology

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Baoshan Luo and Chao Yao},
  title = {QR-FLSB: a Personalized Location Big Data Privacy Protection Model},
  howpublished = {EasyChair Preprint no. 7152},

  year = {EasyChair, 2021}}
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