Journal
2018 26TH EUROMICRO INTERNATIONAL CONFERENCE ON PARALLEL, DISTRIBUTED, AND NETWORK-BASED PROCESSING (PDP 2018)
Volume -, Issue -, Pages 430-435Publisher
IEEE
DOI: 10.1109/PDP2018.2018.00075
Keywords
Privacy; Hierarchical Clustering; Secure Two-Party Computation; Collaborative Clustering
Categories
Funding
- H2020 EU funded project C3ISP [700294]
Ask authors/readers for more resources
This paper presents a general framework for constructing any agglomerative hierarchical clustering algorithm over partitioned data. It is assumed that data is distributed between two (or more) parties horizontally, such that for mutual benefits the participated parties are willing to identify the clusters' structure on their data as a whole, but for privacy restrictions, they avoid to share the original datasets. To this end, in this study, we propose general algorithms based on secure scalar product and secure hamming distance computation to securely compute the desired criteria for shaping the clusters' scheme. The proposed approach covers all possible secure agglomerative hierarchical clustering construction when data is distributed between two (or more) parties, including both numerical and categorical data.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available