The Noisy Max mechanism and its variations are fundamental private selec...
Noisy marginals are a common form of confidentiality-protecting data rel...
When analyzing confidential data through a privacy filter, a data scient...
Differential privacy is a widely accepted formal privacy definition that...
The deep neural network (DNN) models are deemed confidential due to thei...
Sparse histogram methods can be useful for returning differentially priv...
Noninterference offers a rigorous end-to-end guarantee for secure propag...
Differential privacy has become a de facto standard for releasing data i...
As an emerging technique for confidential computing, trusted execution
e...
The permute-and-flip mechanism is a recently proposed differentially pri...
Private selection algorithms, such as the Exponential Mechanism, Noisy M...
In practice, differentially private data releases are designed to suppor...
We propose CheckDP, the first automated and integrated approach for prov...
Differential privacy is an information theoretic constraint on algorithm...
Cache-based side channels enable a dedicated attacker to reveal program
...
Noisy Max and Sparse Vector are selection algorithms for differential pr...
Recent work on formal verification of differential privacy shows a trend...
The widespread acceptance of differential privacy has led to the publica...
This paper investigates a flow- and path-sensitive static information fl...