| Description |
xii, 286 pages ; 24 cm |
| Contents |
The promise of differential privacy -- Basic terms -- Basic techniques and composition theorems -- Releasing linear queries with correlated error -- Generalizations -- Boosting for queries -- When worst-case sensitivity is atypical -- Lower bounds and separations results -- Differential privacy and computational complexity -- Differential privacy and mechanism design -- Differential privacy and machine learning -- Additional models -- Reflections |
| Summary |
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. |
| Bibliography |
Includes bibliographical references |
| Notes |
Originally published: Foundations and trends in theoretical computer science, volume 9, issues 3-4, 2013, pages 211-407 |
| Subject |
Data protection -- Mathematics
|
|
Computer science -- Mathematics
|
| Author |
Roth, Aaron, 1984- author
|
| ISBN |
9781601988188 |
|
1601988184 |
|
