Add to ORKGWe study mechanisms for differential privacy on finite datasets. By deriving sufficient sets for differential privacy we obtain necessary and sufficient conditions for differential privacy, a tight lower bound on the maximal expected error of a discrete mechanism and a characterisation of the optimal mechanism which minimises the maximal expected error within the class of mechanisms considered
Differential privacy, introduced by Dwork et al. in 2006, has become the benchmark for data privacy ...
Differential privacy, introduced by Dwork et al. in 2006, has become the benchmark for data privacy ...
Differential privacy, introduced by Dwork et al. in 2006, has become the benchmark for data privacy ...
We study mechanisms for differential privacy on finite datasets. By deriving sufficient sets for dif...
We study mechanisms for differential privacy on finite datasets. By deriving sufficient sets for di...
We study mechanisms for differential privacy on finite datasets. By deriving sufficient sets for di...
We study mechanisms for differential privacy on finite datasets. By deriving sufficient sets for dif...
We study mechanisms for differential privacy on finite datasets. By deriving sufficient sets for dif...
We study mechanisms for differential privacy on finite datasets. By deriving sufficient sets for dif...
Abstract. The notion of differential privacy has emerged in the area of statisti-cal databases to pr...
Differential privacy is a framework to quantify to what extent individual privacy in a statistical d...
We study Differential Privacy in the abstract setting of Probability on metric spaces. Numerical, c...
Differential privacy is a framework to quantify to what extent individual privacy in a statistical d...
Differential privacy is a framework to quantify to what extent individual privacy in a statistical d...
We examine a generalised Randomised Response (RR) technique in the context of differential privacy a...
Differential privacy, introduced by Dwork et al. in 2006, has become the benchmark for data privacy ...
Differential privacy, introduced by Dwork et al. in 2006, has become the benchmark for data privacy ...
Differential privacy, introduced by Dwork et al. in 2006, has become the benchmark for data privacy ...
We study mechanisms for differential privacy on finite datasets. By deriving sufficient sets for dif...
We study mechanisms for differential privacy on finite datasets. By deriving sufficient sets for di...
We study mechanisms for differential privacy on finite datasets. By deriving sufficient sets for di...
We study mechanisms for differential privacy on finite datasets. By deriving sufficient sets for dif...
We study mechanisms for differential privacy on finite datasets. By deriving sufficient sets for dif...
We study mechanisms for differential privacy on finite datasets. By deriving sufficient sets for dif...
Abstract. The notion of differential privacy has emerged in the area of statisti-cal databases to pr...
Differential privacy is a framework to quantify to what extent individual privacy in a statistical d...
We study Differential Privacy in the abstract setting of Probability on metric spaces. Numerical, c...
Differential privacy is a framework to quantify to what extent individual privacy in a statistical d...
Differential privacy is a framework to quantify to what extent individual privacy in a statistical d...
We examine a generalised Randomised Response (RR) technique in the context of differential privacy a...
Differential privacy, introduced by Dwork et al. in 2006, has become the benchmark for data privacy ...
Differential privacy, introduced by Dwork et al. in 2006, has become the benchmark for data privacy ...
Differential privacy, introduced by Dwork et al. in 2006, has become the benchmark for data privacy ...