Differential Privacy Auditing

Differential Privacy Auditing is a formal verification process that assesses the extent to which a data analysis mechanism adheres to the mathematical guarantees of differential privacy by quantifying the deviation from its prescribed privacy parameters.

Publications

Citations

Authors

Institutions

Publications per year

2017–2026

Parents

Privacy Auditing

Publications

Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment

Rachel Cummings, Damien Desfontaines, David Evans et al. · Harvard Data Science Review · 2024 · 35 citations · Full text

Computing, Computer Science, Systems Engineering +16

Group and Attack: Auditing Differential Privacy

Johan Lokna, Anouk Paradis, Dimitar I. Dimitrov et al. · 2023 · 11 citations · Full text

Computing, Computer Science, Cybersecurity +12

1–2 of 1

Authors

NP
Nicolas Papernot
University of Toronto, Canada
1 publications · 35 citations
1
h-index
WS
Weijie Su
University of Pennsylvania, United States
1 publications · 35 citations
1
h-index
AT
Andreas Terzis
Johns Hopkins University, United States
1 publications · 35 citations
1
h-index
SY
Sergey Yekhanin
Microsoft (United States), United States
1 publications · 35 citations
1
h-index
OO
Olga Ohrimenko
Microsoft Research (United Kingdom), United Kingdom
1 publications · 35 citations
1
h-index

1–5 of 24

Institutions

1–8 of 16

Venues

Harvard Data Science Review
1 publications · 35 citations
1
h-index

1–1