The main idea of DPPro is to project a dataset from a high-dimensional space to a randomly chosen lower- dimensional subspace in order to preserve pairwise L2 dis- tances and thus users segmentation based on these distances.
Aug 9, 2017 · Extensive experimental results demonstrate that DPPro substantially outperforms several state-of-the-art solutions in terms of perturbation ...
DPPro studied by Xu et al. [38] uses a random projection to maximize utility and to preserve pairwise distances between attributes. They add Gaussian noise to ...
Aug 29, 2017 · Abstract—Releasing representative data sets without compro- mising the data privacy has attracted increasing attention from.
Bibliographic details on DPPro: Differentially Private High-Dimensional Data Release via Random Projection.
DPPro, a differentially private algorithm for high-dimensional data release via random projection to maximize utility while guaranteeing privacy, is ...
DPPro [42] utilizes a random projection [24] that preserve L2-distance to the original data in an analyzable form to give a utility guarantee, but this is ...
As a solution, we propose DPRP (Differentially Private Data Release via Random Projections), a reconstruction based approach for releasing differentially ...
Missing: DPPro: Dimensional
In this paper, we propose a differentially private high-dimensional data publication mechanism (DP2-Pub) that runs in two phases: a Markov-blanket-based ...
Our approach leverages generative adversarial nets to generate data and exploits the PATE (Private Aggregation of Teacher Ensembles) framework to protect data ...