The Promise of Differential Privacy: A Tutorial on Algorithmic Techniques
By Cynthia Dwork. 2011
60 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Sociology and Political Science
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Florian Tramèr
- Jonathan Ullman
- Reza Shokri
- Moritz Hardt
- Katrina Ligett
- Aaron Roth
- Cynthia Dwork
- Abhradeep Thakurta
In Sociology and Political Science
- Moritz Hardt
- Katrina Ligett
- Florian Tramèr
- Reza Shokri
- Frank McSherry
- Fatemehsadat Mireshghallah
- Aaron Roth
- Arjun Narayan
Citations in the corpus
- Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing (2014, neutral): “Differential privacy (DP) is a popular framework for designing statistical release mechanisms, and is often proposed as a solution to privacy concerns in medical settings [10, 12, 45, 47].”
- A Simple and Practical Algorithm for Differentially Private Data Release (2010, background): “In recent years, the theoretical literature on differential privacy has provided a large repertoire of techniques for achieving the definition in a variety of settings (see, e.g., [8, 9]).”
- What Does it Mean for a Language Model to Preserve Privacy? (2022, background): “These strong guarantees have at times been interpreted as a “promise” to users [27], that their secrets will be protected regardless of their decision to share their data.”