Dependent differential privacy:Gaussian mechanism for correlated datasets
Differential Privacy is a data perturbation framework,which ensures that the query results are not distinguishable in probability.Research shows that when differential privacy is applied to associated data sets,it will bring the risk of privacy disclo-sure.Based on the dependent differential privacy,this paper quantifies the sensitivity of the dependent differential privacy;Then,a Gaussian Mechanism Algorithm-Dependent Differential Privacy is proposed to realize data disturbance,and the basic theorem that the mechanism meets the privacy guarantee is proved;Experiments using real data sets show that GMA-DDP has high availa-bility in managing privacy utility tradeoffs that depend on data.
万方数据
维普
