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Predicate-Tree Based Pretty Good Privacy of Data

机译:基于谓词树的数据相当好的隐私

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摘要

Growth of Internet has led to exponential rise in data communication over the World Wide Web. Several applications and entities such as online banking transactions, stock trading, e-commerce Web sites, etc. are at a constant risk of eavesdropping and hacking. Hence, security of data is of prime concern. Recently, vertical data have gained lot of focus because of their significant performance benefits over horizontal data in various data mining applications. In our current work, we propose a Predicate-Tree based solution for protection of data. Predicate-Trees or pTrees are compressed, data-mining-ready, vertical data structures and have been used in a plethora of data-mining research areas such as spatial association rule mining, text clustering, closed k-nearest neighbor classification, etc. We show how for data mining purposes, the scrambled pTrees would be unrevealing of the raw data to anyone except for the authorized person issuing a data mining request. In addition, we propose several techniques which come along as a benefit of using vertical pTrees. To the best of our knowledge, our approach is novel and provides sufficient speed and protection level for an effective data security.
机译:互联网的发展导致通过互联网进行的数据通信呈指数增长。诸如在线银行交易,股票交易,电子商务网站之类的若干应用程序和实体始终处于窃听和黑客攻击的风险中。因此,数据的安全性是首要考虑的问题。最近,垂直数据由于在各种数据挖掘应用程序中比水平数据具有显着的性能优势而备受关注。在我们当前的工作中,我们提出了一种基于谓词树的解决方案来保护数据。谓词树或pTree是经过压缩的,可进行数据挖掘的垂直数据结构,并且已在众多数据挖掘研究领域中使用,例如空间关联规则挖掘,文本聚类,封闭的k最近邻分类等。演示了出于数据挖掘的目的,加扰的pTree将如何向任何人透露原始数据,除非授权人员发出了数据挖掘请求。另外,我们提出了几种使用垂直pTree的好处。据我们所知,我们的方法是新颖的,为有效的数据安全性提供了足够的速度和保护级别。

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