Secure model for clustering distributed data

Sumana Maradithaya; K. S. Hareesha

doi:10.1109/ICACCI.2017.8126014

Secure model for clustering distributed data

Sumana Maradithaya, K. S. Hareesha

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

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 language	English
Title of host publication	2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1256-1260
Number of pages	5
Volume	2017-January
ISBN (Electronic)	9781509063673
DOIs	https://doi.org/10.1109/ICACCI.2017.8126014
Publication status	Published - 30-11-2017
Event	2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017 - Manipal, Mangalore, India Duration: 13-09-2017 → 16-09-2017

Conference

Conference	2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017
Country/Territory	India
City	Manipal, Mangalore
Period	13-09-17 → 16-09-17

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Computer Science Applications
Information Systems

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10.1109/ICACCI.2017.8126014

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title = "Secure model for clustering distributed data",

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.",

author = "Sumana Maradithaya and Hareesha, {K. S.}",

year = "2017",

month = nov,

day = "30",

doi = "10.1109/ICACCI.2017.8126014",

language = "English",

volume = "2017-January",

pages = "1256--1260",

booktitle = "2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017 ; Conference date: 13-09-2017 Through 16-09-2017",

}

Maradithaya, S & Hareesha, KS 2017, Secure model for clustering distributed data. in 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017. vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 1256-1260, 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, Manipal, Mangalore, India, 13-09-17. https://doi.org/10.1109/ICACCI.2017.8126014

TY - GEN

T1 - Secure model for clustering distributed data

AU - Maradithaya, Sumana

AU - Hareesha, K. S.

PY - 2017/11/30

Y1 - 2017/11/30

N2 - 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.

AB - 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.

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UR - http://www.scopus.com/inward/citedby.url?scp=85042771073&partnerID=8YFLogxK

U2 - 10.1109/ICACCI.2017.8126014

DO - 10.1109/ICACCI.2017.8126014

M3 - Conference contribution

AN - SCOPUS:85042771073

VL - 2017-January

SP - 1256

EP - 1260

BT - 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017

Y2 - 13 September 2017 through 16 September 2017

ER -

Secure model for clustering distributed data

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