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Watermarking Relational Databases

Watermarking Relational Databases
关系数据库加水印
批准号:
0242421
负责人:
Sunil Prabhakar
金额:
$15.0万
依托单位:
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2003
资助国家:
美国
项目状态:
已结题
起止时间:
2003-06-01 至 2007-05-31

项目摘要

项目成果

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中文摘要
翻译
水印是将不可检测的信息嵌入到对象(如图像和视频)中的技术,主要是通过实现对内容的可证明所有权来保护数据免受未经授权的复制和传播。虽然在这个专题上投入了大量的工作,但在关系数据库领域实现同样的概念的工作却很少。特别是考虑到当前业务交互向分布式计算技术的主流迁移,可以从为关系数据库添加水印的能力中获益良多。有效的水印可以减少供应商不愿提供公共可用性或共享有价值的数据。与多媒体相比,关系数据领域的水印提出了一系列全新的挑战。关系数据的特征是存储在实际值中的值以及这些数据的组织。关系数据库水印的一个主要挑战就是“带宽不足”,这源于该领域中固有的一个主要噪声成分的缺乏。为了进一步增加挑战,这些约束是依赖于数据和应用程序的。这个项目探索了有价值的外包关系数据的水印问题。特别是,它涉及:弹性水印方法的设计,数字关系数据的概念验证实现,在真实外包商业数据上的部署,以及算法的弹性评估。
英文摘要
Watermarking is the technique of embedding un-detectable information into objects (e.g. images and video) mainly to protect the data from unauthorized duplication and distribution by enabling provable ownership over the content. Whereas considerable work has been invested in this topic, little has been done to enable the same concept in the area of relational databases. There is much to be gained from the ability to watermark relational databases, in particular considering current mainstream migration of business interactions towards distributed computing technologies. Effective watermarking can decrease the reluctance of vendors to provide public availability or sharing of valuable data.Compared to multimedia, watermarking in the area of relational data presents a whole new set of challenges. Relational data are characterized by value lying both in the actual values stored as well as the organization of these data. A major challenge for watermarking relational databases is simply the "lack of bandwidth", deriving from the inherent lack of a major noise component in that domain. To further add to the challenge, these constraints are data and application dependent.This project explores the issue of watermarking valuable outsourced relational data. In particular, it addresses: the design of a resilient watermarking method, a proof-of-concept implementation for numeric relational data, deployment on real outsourced commercial data, and an evaluation of the resilience of the algorithm.
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