期刊
IEEE NETWORK
卷 34, 期 2, 页码 52-56出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/MNET.001.1900139
关键词
Privacy; Trajectory; Market research; Information services; Data privacy; Artificial neural networks; Internet of Things
类别
资金
- National Key Research and Development Plan [2018YFB0803504]
- National Natural Science Foundation of China [61871140, U1636215]
- Guangdong Province Key Research and Development Plan [2019B010137004]
- Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme
LPPMs have been well surveyed based on the view of methodologies, attacks, and so on. It has long been ignored that LBS applicability will be sacrificed due to the implementation of LPPMs. Existing and future LPPMs should clearly claim LBS applicability. This is essential for future research on location privacy in the MEC environment because of the diversity development of the LBS. To realize this goal, in this article, we present a classification of LBS by dividing the LBS into three independent dimensions, and we list a brief sample of LBS applicability of typical LPPMs based on our LBS categories. Then we consider even further new trends of LBS in MEC and introduce a new type of LBS named DLBS. We explain that DLBS subverts the existing LPPM problem, and we present a conceptual method to cope with the location privacy threat from DLBS.
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