Sep 30, 2009 · We present instantiations of the proposed framework for both ordinal and nominal data, and we provide a theoretical analysis on their privacy ...

The core of our solution is a framework that applies wavelet transforms on the data before adding noise to it. We present instantiations of the proposed ...

In this paper, we develop a data publishing technique that ensures \epsilon-differential privacy while providing accurate answers for range-count queries.

In this paper, we develop a data publishing technique that ensures \epsilon-differential privacy while providing accurate answers for range-count queries.

In this paper, we develop a data publishing technique that ensures ∈-differential privacy while providing accurate answers for range-count queries.

Sep 12, 2022 · The analysis shows that using Haar wavelet transform and Gaussian mechanism, we can preserve the differential privacy for each input data and ...

Aug 25, 2015 · ... Differential privacy (DP) has been proposed to provide rigorous privacy guarantees, ensuring that any data sample or user's data at any ...

This study presents a research on differential privacy for range query via Haar wavelet transform and Gaussian mechanism. First, the noise injected into the ...

Mar 18, 2013 · Download Differential Privacy via Wavelets for free. This is the code used in the experiments of the following paper: Xiaokui Xiao, ...

We propose a differential privacy preserving Naive Bayes classification algorithm via wavelet transform.