Privacy-preserving k -means clustering over vertically partitioned data
Abstract
Privacy and security concerns can prevent sharing of data, derailing data mining projects. Distributed knowledge discovery, if done correctly, can alleviate this problem. The key is to obtain valid results, while providing guarantees on the (non)disclosure of data. We present a method for k-means clustering when different sites contain different attributes for a common set of entities. Each site learns the cluster of each entity, but learns nothing about the attributes at other sites.
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Based on similarity of clients' data, different groups can be formed and a different model can be trained for each similar cluster.
“Moreover, based on similarity of clients' data, different groups can be formed and a different model can be trained for each similar cluster [97].”
From Challenges, Applications and Design Aspects of Federated Learning: A Survey (2021) · cites this paper as [97]
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- Authors
- Vaidya, J., & Clifton, C.
- Affiliations
- Purdue University West Lafayette (US)
- Venue
- Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2003-08-24
- Pages
- 206-215
- OpenAlex
- W2139336600
- Cited by
- 626
- References
- 32
- Citations by year
- 2026: 1 · 2025: 8 · 2024: 8 · 2023: 9 · 2022: 8 · 2021: 22 · 2020: 28 · 2019: 29 · 2018: 36 · 2017: 15 · 2016: 25 · 2015: 24 · 2014: 26 · 2013: 46 · 2012: 40
- FWCI
- 19.84
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
- Keywords
- privacy-preserving data mining, K-means clustering, vertically partitioned data, distributed knowledge discovery, attribute privacy
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