Collecting Geospatial Data with Local Differential Privacy for Personalized Service
- Title
- Collecting Geospatial Data with Local Differential Privacy for Personalized Service
- Author
- 정우환
- Keywords
- local differential privacy; geospatial data
- Issue Date
- 2021-04
- Publisher
- IEEE
- Citation
- 2021 IEEE 37th International Conference on Data Engineering (ICDE), Page. 2237-2242
- Abstract
- Geospatial data provides a lot of benefits for personalized services. However, since the geospatial data contains
sensitive information about personal activities, collecting the raw
data has a potential risk of leaking private information from
the collectors. Recently, local differential privacy (LDP), which
protects the privacy of users without trusting the collector, has
been adopted to preserve privacy in many real applications.
However, most of existing LDP algorithms focus on obtaining
aggregated values such as mean and histogram from the collected
data. In this paper, we investigate the problem of collecting
the locations of individual users under LDP, and propose a
perturbation mechanism designed carefully to reduce the error
of each perturbed location according to the privacy budget and
the domain size. In addition, we show the effectiveness of the
proposed algorithm through experiments on various real datasets.
- URI
- https://ieeexplore.ieee.org/document/9458926https://repository.hanyang.ac.kr/handle/20.500.11754/172289
- DOI
- 10.1109/ICDE51399.2021.00230
- Appears in Collections:
- ETC[S] > 연구정보
- Files in This Item:
There are no files associated with this item.
- Export
- RIS (EndNote)
- XLS (Excel)
- XML