Privacy-preserving Analysis of Correlated Data

By Yizhen Wang, Shuang Song, Kamalika Chaudhuri. 2016

14 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Abhishek Singh
2. Praneeth Vepakomma
3. Ramesh Raskar
4. Samuel Haney
5. John M. Abowd
6. Fatemehsadat Mirshghallah
7. Ashwin Machanavajjhala
8. Mohammadkazem Taram

Citations in the corpus

• Privacy in Deep Learning: A Survey (2020, methodology): “The Pufferfish framework can be used to create new privacy definitions tailored for specific applications [63], such as Census data release.”
• Utility Cost of Formal Privacy for Releasing National Employer-Employee Statistics (2017, background): “These have led to the design of application specific privacy notions (e.g., [16, 39]).”