Fault tolerant secure K-means clustering data mining on semi-honest parties

This project report will discuss the design of secure fault tolerant k-means clustering data mining on semi-honest parties. The algorithm will protect all parties’ privacy while using caching and checkpointing to tolerate unexpected fault exceptions, such as party dies during processing. Sha...

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Main Author:	Qian, Xiaofeng
Other Authors:	Ng Wee Keong
Format:	Final Year Project
Language:	English
Published:	2010
Subjects:	DRNTU::Engineering::Computer science and engineering::Data::Data encryption
Online Access:	http://hdl.handle.net/10356/36286
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Institution:	Nanyang Technological University
Language:	English

id	sg-ntu-dr.10356-36286
record_format	dspace
spelling	sg-ntu-dr.10356-362862023-03-03T20:59:06Z Fault tolerant secure K-means clustering data mining on semi-honest parties Qian, Xiaofeng Ng Wee Keong School of Computer Engineering Centre for Advanced Information Systems DRNTU::Engineering::Computer science and engineering::Data::Data encryption This project report will discuss the design of secure fault tolerant k-means clustering data mining on semi-honest parties. The algorithm will protect all parties’ privacy while using caching and checkpointing to tolerate unexpected fault exceptions, such as party dies during processing. Shair Secret Sharing Scheme and Secure Scalar Product will be used to maintain caching data. The algorithm fault tolerance threshold is (k, N), which means up to N-k parties dying is tolerable. Other different types of faults are also handled using different ways. Bachelor of Engineering (Computer Engineering) 2010-04-30T03:49:19Z 2010-04-30T03:49:19Z 2010 2010 Final Year Project (FYP) http://hdl.handle.net/10356/36286 en Nanyang Technological University 75 p. application/pdf
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
language	English
topic	DRNTU::Engineering::Computer science and engineering::Data::Data encryption
spellingShingle	DRNTU::Engineering::Computer science and engineering::Data::Data encryption Qian, Xiaofeng Fault tolerant secure K-means clustering data mining on semi-honest parties
description	This project report will discuss the design of secure fault tolerant k-means clustering data mining on semi-honest parties. The algorithm will protect all parties’ privacy while using caching and checkpointing to tolerate unexpected fault exceptions, such as party dies during processing. Shair Secret Sharing Scheme and Secure Scalar Product will be used to maintain caching data. The algorithm fault tolerance threshold is (k, N), which means up to N-k parties dying is tolerable. Other different types of faults are also handled using different ways.
author2	Ng Wee Keong
author_facet	Ng Wee Keong Qian, Xiaofeng
format	Final Year Project
author	Qian, Xiaofeng
author_sort	Qian, Xiaofeng
title	Fault tolerant secure K-means clustering data mining on semi-honest parties
title_short	Fault tolerant secure K-means clustering data mining on semi-honest parties
title_full	Fault tolerant secure K-means clustering data mining on semi-honest parties
title_fullStr	Fault tolerant secure K-means clustering data mining on semi-honest parties
title_full_unstemmed	Fault tolerant secure K-means clustering data mining on semi-honest parties
title_sort	fault tolerant secure k-means clustering data mining on semi-honest parties
publishDate	2010
url	http://hdl.handle.net/10356/36286
_version_	1759854046310563840

Fault tolerant secure K-means clustering data mining on semi-honest parties

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