The Algorithmic Foundations of Differential Privacy 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024


The Algorithmic Foundations of Differential Privacy

简体网页||繁体网页
Cynthia Dwork 作者
Now Publishers Inc
译者
2014-8-11 出版日期
300 页数
USD 99.00 价格
Paperback
丛书系列
9781601988188 图书编码

The Algorithmic Foundations of Differential Privacy 在线电子书 图书标签: 计算机  privacy  Cynthia_Dwork   


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The Algorithmic Foundations of Differential Privacy 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

The Algorithmic Foundations of Differential Privacy 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

The Algorithmic Foundations of Differential Privacy 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024



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最好能够对密码学和AGT有一定了解,这样能够比较顺畅地通读全本(虽然能看出老太太已经尽可能避免用密码解决问题…

评分

最好能够对密码学和AGT有一定了解,这样能够比较顺畅地通读全本(虽然能看出老太太已经尽可能避免用密码解决问题…

评分

最好能够对密码学和AGT有一定了解,这样能够比较顺畅地通读全本(虽然能看出老太太已经尽可能避免用密码解决问题…

评分

最好能够对密码学和AGT有一定了解,这样能够比较顺畅地通读全本(虽然能看出老太太已经尽可能避免用密码解决问题…

评分

最好能够对密码学和AGT有一定了解,这样能够比较顺畅地通读全本(虽然能看出老太太已经尽可能避免用密码解决问题…

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The Algorithmic Foundations of Differential Privacy 在线电子书 pdf 下载 txt下载 epub 下载 mobi 在线电子书下载

The Algorithmic Foundations of Differential Privacy 在线电子书 图书描述

The problem of privacy-preserving data analysis has a long history spanning multiple disciplines. As electronic data about individuals becomes increasingly detailed, and as technology enables ever more powerful collection and curation of these data, the need increases for a robust, meaningful, and mathematically rigorous definition of privacy, together with a computationally rich class of algorithms that satisfy this definition. Differential Privacy is such a definition. After motivating and discussing the meaning of differential privacy, the preponderance of this book is devoted to fundamental techniques for achieving differential privacy, and application of these techniques in creative combinations, using the query-release problem as an ongoing example. A key point is that, by rethinking the computational goal, one can often obtain far better results than would be achieved by methodically replacing each step of a non-private computation with a differentially private implementation. Despite some astonishingly powerful computational results, there are still fundamental limitations - not just on what can be achieved with differential privacy but on what can be achieved with any method that protects against a complete breakdown in privacy. Virtually all the algorithms discussed in this book maintain differential privacy against adversaries of arbitrary computational power. Certain algorithms are computationally intensive, others are efficient. Computational complexity for the adversary and the algorithm are both discussed. As the book progresses, it turns from fundamentals to applications other than query-release, discussing differentially private methods for mechanism design and machine learning. The vast majority of the literature on differentially private algorithms considers a single, static, database that is subject to many analyses. Differential privacy in other models, including distributed databases and computations on data streams is also discussed in the book.

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