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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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2010
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| Online Access: | http://hdl.handle.net/10356/39732 |
| _version_ | 1811685709798440960 |
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| author | Herianto, Andre Ricardo. |
| author2 | Ng Wee Keong |
| author_facet | Ng Wee Keong Herianto, Andre Ricardo. |
| author_sort | Herianto, Andre Ricardo. |
| collection | NTU |
| 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. |
| first_indexed | 2024-10-01T04:48:50Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/39732 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:48:50Z |
| publishDate | 2010 |
| record_format | dspace |
| spelling | ntu-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 |
| spellingShingle | DRNTU::Engineering::Computer science and engineering::Data::Data encryption Herianto, Andre Ricardo. Fault tolerant privacy preseving decision tree induction |
| title | 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_short | Fault tolerant privacy preseving decision tree induction |
| title_sort | fault tolerant privacy preseving decision tree induction |
| topic | DRNTU::Engineering::Computer science and engineering::Data::Data encryption |
| url | http://hdl.handle.net/10356/39732 |
| work_keys_str_mv | AT heriantoandrericardo faulttolerantprivacypresevingdecisiontreeinduction |