Oct 12, 2015 · We investigate a relaxation of DP, by considering prior distributions that capture more reasonable amounts of background knowledge.
ABSTRACT. Differential privacy (DP) has become widely accepted as a rigorous definition of data privacy, with stronger privacy.
Differential privacy (DP) has become widely accepted as a rigorous definition of data privacy, with stronger privacy guarantees than traditional statistical ...
It is shown that for different privacy budgets, DP can be used to achieve membership privacy for various adversarial settings, thus leading to an ...
ABSTRACT. Differential privacy (DP) has become widely accepted as a rigorous definition of data privacy, with stronger privacy.
Differential privacy (DP) has become widely accepted as a rigorous definition of data privacy, with stronger privacy guarantees than traditional statistical ...
Differential privacy (DP) has become widely accepted as a rigorous definition of data privacy, with stronger privacy guarantees than traditional statistical ...
Bibliographic details on Differential Privacy with Bounded Priors: Reconciling Utility and Privacy in Genome-Wide Association Studies.
Tramer F, Huang Z, Hubaux J and Ayday E, Differential privacy with bounded priors: Reconciling utility and privacy in genome-wide association studies, in CCS, ...
This will be a graduate level seminar-style course introducing differential privacy and some of its applications.