Differential Privacy (Record no. 85108)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 04046nam a22005295i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-031-02350-7 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240730163911.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 220601s2017 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783031023507 |
| -- | 978-3-031-02350-7 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 005.8 |
| 100 1# - AUTHOR NAME | |
| Author | Li, Ninghui. |
| 245 10 - TITLE STATEMENT | |
| Title | Differential Privacy |
| Sub Title | From Theory to Practice / |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2017. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XIII, 124 p. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Synthesis Lectures on Information Security, Privacy, and Trust, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Acknowledgments -- Introduction -- A Primer on ?-Differential Privacy -- What Does DP Mean? -- Publishing Histograms for Low-dimensional Datasets -- Differentially Private Optimization -- Publishing Marginals -- The Sparse Vector Technique -- Bibliography -- Authors' Biographies. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Over the last decade, differential privacy (DP) has emerged as the de facto standard privacy notion for research in privacy-preserving data analysis and publishing. The DP notion offers strong privacy guarantee and has been applied to many data analysis tasks. This Synthesis Lecture is the first of two volumes on differential privacy. This lecture differs from the existing books and surveys on differential privacy in that we take an approach balancing theory and practice. We focus on empirical accuracy performances of algorithms rather than asymptotic accuracy guarantees. At the same time, we try to explain why these algorithms have those empirical accuracy performances. We also take a balanced approach regarding the semantic meanings of differential privacy, explaining both its strong guarantees and its limitations. We start by inspecting the definition and basic properties of DP, and the main primitives for achieving DP. Then, we give a detailed discussion on the the semantic privacy guarantee provided by DP and the caveats when applying DP. Next, we review the state of the art mechanisms for publishing histograms for low-dimensional datasets, mechanisms for conducting machine learning tasks such as classification, regression, and clustering, and mechanisms for publishing information to answer marginal queries for high-dimensional datasets. Finally, we explain the sparse vector technique, including the many errors that have been made in the literature using it. The planned Volume 2 will cover usage of DP in other settings, including high-dimensional datasets, graph datasets, local setting, location privacy, and so on. We will also discuss various relaxations of DP. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Lyu, Min. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Su, Dong. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Yang, Weining. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-3-031-02350-7 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cham : |
| -- | Springer International Publishing : |
| -- | Imprint: Springer, |
| -- | 2017. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
| -- | cr |
| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
| -- | |
| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data protection. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data and Information Security. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 1945-9750 |
| 912 ## - | |
| -- | ZDB-2-SXSC |
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