Collecting Geospatial Data Under Local Differential Privacy With Improving Frequency Estimation
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hong, Daeyoung | - |
dc.contributor.author | Jung, Woohwan | - |
dc.contributor.author | Shim, Kyuseok | - |
dc.date.accessioned | 2023-08-07T07:30:14Z | - |
dc.date.available | 2023-08-07T07:30:14Z | - |
dc.date.issued | 2023-07 | - |
dc.identifier.issn | 1041-4347 | - |
dc.identifier.issn | 1558-2191 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/113688 | - |
dc.description.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. In this paper, we investigate the problem of collecting the locations of individual users under LDP, and propose a perturbation mechanism designed carefully to minimize the expected error of perturbed locations according to the privacy budget and the data domain. The frequency distribution of perturbed locations inevitably has a large error. To tackle the problem, we also propose a postprocessing algorithm to estimate the original frequency distribution of collected data by using convex optimization. By experiments with various real datasets, we show the effectiveness of the proposed algorithms. | - |
dc.format.extent | 13 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Institute of Electrical and Electronics Engineers | - |
dc.title | Collecting Geospatial Data Under Local Differential Privacy With Improving Frequency Estimation | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.1109/TKDE.2022.3181049 | - |
dc.identifier.wosid | 001004293600015 | - |
dc.identifier.bibliographicCitation | IEEE Transactions on Knowledge and Data Engineering, v.35, no.7, pp 6739 - 6751 | - |
dc.citation.title | IEEE Transactions on Knowledge and Data Engineering | - |
dc.citation.volume | 35 | - |
dc.citation.number | 7 | - |
dc.citation.startPage | 6739 | - |
dc.citation.endPage | 6751 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordPlus | RANGE QUERIES | - |
dc.subject.keywordAuthor | Frequency estimation | - |
dc.subject.keywordAuthor | Differential privacy | - |
dc.subject.keywordAuthor | Privacy | - |
dc.subject.keywordAuthor | Servers | - |
dc.subject.keywordAuthor | Frequency-domain analysis | - |
dc.subject.keywordAuthor | Geospatial analysis | - |
dc.subject.keywordAuthor | Estimation | - |
dc.subject.keywordAuthor | Local differential privacy | - |
dc.subject.keywordAuthor | geospatial data | - |
dc.subject.keywordAuthor | frequency estimation | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/9792226?arnumber=9792226&SID=EBSCO:edseee | - |
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