Fault tolerant privacy preseving decision tree induction

Privacy-Preserving Data Mining (PPDM) is an emerging technology that allows many parties to gain a special knowledge of their combined information. However, this information usually contains private data that can not be disclosed to any parties. Various techniques and algorithms have been p...

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Main Author: Herianto, Andre Ricardo.
Other Authors: Ng Wee Keong
Format: Final Year Project
Language:English
Published: 2010
Subjects:
Online Access:http://hdl.handle.net/10356/39732
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-397322023-03-03T20:43:54Z Fault tolerant privacy preseving decision tree induction Herianto, Andre Ricardo. Ng Wee Keong School of Computer Engineering Centre for Advanced Information Systems DRNTU::Engineering::Computer science and engineering::Data::Data encryption Privacy-Preserving Data Mining (PPDM) is an emerging technology that allows many parties to gain a special knowledge of their combined information. However, this information usually contains private data that can not be disclosed to any parties. Various techniques and algorithms have been proposed and developed to achieve the goal without compromising individual privacy. These techniques usually depend highly on Secure Multi-Party Computation (SMC) protocol that makes use of complex cryptography protocol. These cryptography protocols alone are very expensive and usually have considerably a huge time complexity especially in high dimensional and huge dataset. Combined with the nature of the data mining algorithm that is an iterative process and also current network infrastructure that is considerably slow compared with the current computer processing speed, PPDM is extremely expensive process. In order to exchange data between parties in PPDM algorithm, we require network infrastructure. As we know, nowadays our network infrastructure is not reliable enough to guarantee its service. As a result, there is a probability that a network failure might occur in the middle of the algorithm execution. Considering that PPDM algorithm can spend days or months in order to complete its process, it would be very expensive to reexecute the algorithm each time a network failure occurs. In this paper, we would suggest a system that could handle a certain level of network failure to avoid re-executing the algorithm over and over from beginning. We will examine the algorithm and its secure protocol step by step and suggest many techniques in order to handle each case by case scenario of network failure that might happen anytime in the process. Bachelor of Engineering (Computer Science) 2010-06-03T07:34:29Z 2010-06-03T07:34:29Z 2010 2010 Final Year Project (FYP) http://hdl.handle.net/10356/39732 en Nanyang Technological University 66 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
Herianto, Andre Ricardo.
Fault tolerant privacy preseving decision tree induction
description Privacy-Preserving Data Mining (PPDM) is an emerging technology that allows many parties to gain a special knowledge of their combined information. However, this information usually contains private data that can not be disclosed to any parties. Various techniques and algorithms have been proposed and developed to achieve the goal without compromising individual privacy. These techniques usually depend highly on Secure Multi-Party Computation (SMC) protocol that makes use of complex cryptography protocol. These cryptography protocols alone are very expensive and usually have considerably a huge time complexity especially in high dimensional and huge dataset. Combined with the nature of the data mining algorithm that is an iterative process and also current network infrastructure that is considerably slow compared with the current computer processing speed, PPDM is extremely expensive process. In order to exchange data between parties in PPDM algorithm, we require network infrastructure. As we know, nowadays our network infrastructure is not reliable enough to guarantee its service. As a result, there is a probability that a network failure might occur in the middle of the algorithm execution. Considering that PPDM algorithm can spend days or months in order to complete its process, it would be very expensive to reexecute the algorithm each time a network failure occurs. In this paper, we would suggest a system that could handle a certain level of network failure to avoid re-executing the algorithm over and over from beginning. We will examine the algorithm and its secure protocol step by step and suggest many techniques in order to handle each case by case scenario of network failure that might happen anytime in the process.
author2 Ng Wee Keong
author_facet Ng Wee Keong
Herianto, Andre Ricardo.
format Final Year Project
author Herianto, Andre Ricardo.
author_sort Herianto, Andre Ricardo.
title Fault tolerant privacy preseving decision tree induction
title_short Fault tolerant privacy preseving decision tree induction
title_full Fault tolerant privacy preseving decision tree induction
title_fullStr Fault tolerant privacy preseving decision tree induction
title_full_unstemmed Fault tolerant privacy preseving decision tree induction
title_sort fault tolerant privacy preseving decision tree induction
publishDate 2010
url http://hdl.handle.net/10356/39732
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