Secure model for clustering distributed data

Sumana Maradithaya, K. S. Hareesha

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Data of similar nature are disseminated across organizations and needs to be analyzed to discover patterns and obtain relevant conclusions. While mining distributed data, disclosure of the sensitive information is a limitation that needs to be handled. This paper focuses on the construction of one such privacy preserving clustering approach that clusters, scattered data using the k-means strategy securely. The proposed approach provides maximum security of the sensitive data while modeling and also generates accurate results in comparison to the past related approaches.

Original languageEnglish
Title of host publication2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1256-1260
Number of pages5
Volume2017-January
ISBN (Electronic)9781509063673
DOIs
Publication statusPublished - 30-11-2017
Event2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017 - Manipal, Mangalore, India
Duration: 13-09-201716-09-2017

Conference

Conference2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017
CountryIndia
CityManipal, Mangalore
Period13-09-1716-09-17

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All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems

Cite this

Maradithaya, S., & Hareesha, K. S. (2017). Secure model for clustering distributed data. In 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017 (Vol. 2017-January, pp. 1256-1260). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICACCI.2017.8126014