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Predicate-Tree Based Pretty Good Privacy of Data

机译:基于谓词树的数据

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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:互联网的增长导致了万维网数据通信的指数上升。在网上银行交易,股票交易,电子商务网站等的若干应用和实体处于窃听和黑客障碍的持续风险。因此,数据的安全性是主要关注的。最近,垂直数据获得了很多焦点,因为它们在各种数据挖掘应用中的水平数据中的显着性能优势。在我们当前的工作中,我们提出了一种基于树的谓语树,以保护数据保护。谓词树或港口被压缩,数据采矿准备好,垂直数据结构,并已用于诸如空间关联规则挖掘,文本聚类,闭合k最近邻分类等中的数据挖掘研究领域。我们除了发出数据挖掘请求的授权人员外,扰乱的PTREE将使扰乱的PTREE对任何人都肆无忌惮地毫不赘述。此外,我们提出了几种使用垂直港口的益处的技术。据我们所知,我们的方法是新颖的,为有效的数据安全提供了足够的速度和保护水平。

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