With the recent increase in the use of location-based services (LBSs) due to the development of wireless communication technology and positioning technology, we have started to recognize location data as an important resource. With advanced positioning technology, it is possible to acquire accurate location data, but users are at risk of privacy exposure. Currently, many researchers are studying privacy algorithms for data anonymization. However, few studies have applied location data. In this paper, we introduce an anonymization algorithm for location privacy of the LBS model. Then we classify each algorithm into location k-anonymity and location differential privacy, and compare and analyze each algorithm.
Bibliographical notePublisher Copyright:
© 2018 Pushpa Publishing House, Allahabad, India.
All Science Journal Classification (ASJC) codes
- Atomic and Molecular Physics, and Optics