Privacy Preserving of Sequential Pattern Mining
碩士 === 靜宜大學 === 資訊管理學系研究所 === 94 === Enterprises are making a lot of advantages from data sharing, in the meantime, they are concerning about the sensitive information leaking to the competitors, which will influence company’s profits. Recently, there are more and more activities that associate with...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2006
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| Online Access: | http://ndltd.ncl.edu.tw/handle/gk3r58 |
| Summary: | 碩士 === 靜宜大學 === 資訊管理學系研究所 === 94 === Enterprises are making a lot of advantages from data sharing, in the meantime, they are concerning about the sensitive information leaking to the competitors, which will influence company’s profits. Recently, there are more and more activities that associate with Data Mining (DM) studies. It causes more risk of the critical information flowing outward business. Privacy preserving thereafter becomes one of popular research topics. However, Privacy Preserving on Sequential Pattern Mining is still not well investigated. The thesis focuses on privacy issue on sequential pattern mining. Here we propose three effective sequential pattern hiding algorithms which keep data sharing and preserve sensitve information at the same time.
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