다자간 환경에서 프라이버시를 보호하는 효율적인 DBSCAN 군집화 기법Practical Privacy-Preserving DBSCAN Clustering Over Horizontally Partitioned Data
- Other Titles
- Practical Privacy-Preserving DBSCAN Clustering Over Horizontally Partitioned Data
- Authors
- 김기성; 정익래
- Issue Date
- 2010
- Publisher
- 한국정보보호학회
- Keywords
- Privacy; DBSCAN; Clustering
- Citation
- 정보보호학회논문지, v.20, no.3, pp.105 - 111
- Indexed
- KCI
- Journal Title
- 정보보호학회논문지
- Volume
- 20
- Number
- 3
- Start Page
- 105
- End Page
- 111
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/118419
- ISSN
- 1598-3986
- Abstract
- We propose a practical privacy-preserving clustering protocol over horizontally partitioned data. We extend the DBSCAN clustering algorithm into a distributed protocol in which data providers mix real data with fake data to provide privacy. Our privacy-preserving clustering protocol is very efficient whereas the previous privacy-preserving protocols in the distributed environments are not practical to be used in real applications. The efficiency of our privacy-preserving clustering protocol over horizontally partitioned data is comparable with those of privacy-preserving clustering protocols in the non-distributed environments.
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Collections - School of Cyber Security > Department of Information Security > 1. Journal Articles
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