保障大数据隐私

K. Chaudhary, Sahil Yadav, T. Singh, Dhruv Yaduvanshi, Monika Goyal
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引用次数: 0

摘要

巨大的是一个术语,用于惊人的巨大的启发性列表,这些列表具有逐渐多样化和复合的结构。此外,照顾、分析和应用进一步的策略来隔离结果等额外的困难是不可分割的。这一术语用于描述研究大量复杂信息以分离游戏示例或识别隐藏关系的路线。无论如何,在大信息中,安全与不同担忧之间存在着明显的逻辑不一致。本文的主要关注点和理由是在海量信息中对超然和安全的担忧。本文讨论了t-接近、k-隐晦、l-组合多样性和差分安全性等不同的未知保护守恒方法的研究和整理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ensuring Privacy in Big Data
Enormous is characterized as a term used for astoundingly enormous enlightening lists that have progressively various and composite structure. Further, additional difficulties like taking care of, analyzing and applying further strategies for isolating outcomes come inseparable. It is the term used to depict the route toward investigating a lot of complex information to part with the game examples or recognize concealed relationships. In any case, there is a conspicuous logical inconsistency between the security and different worries in large information. The principal concern and reason for this paper is on detachment and security worries in enormous information. This paper speaks to on investigation and arrangement of different unknown methods for protection conservation like t-proximity, k-obscurity, l-assorted variety and differential security.
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