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The main contribution of this paper lies in the algorithm to accurately reconstruct the community joint density given the perturbed multidimensional stream data information. Any statistical question about the community can be answered using the reconstructed joint density. There have been many efforts on the community distribution reconstruction. In this project, we are considering the information privacy which now-a-days has become one of the most important issues. We touch upon several techniques of masking the data, namely random distortion, including the uniform and Gaussian noise, applied to the data in order to protect it. Then, after using a certain data recovering techniques we look for the distribution of data obtained. Our task is to determine whether the distributions of the original and recovered data are close enough to each other despite the nature of the noise applied. We are considering an ensemble clustering method to reconstruct the initial data distribution. As the tool for the algorithm implementations we chose the “language of choice in industrial world” – MATLAB. KeywordsPerturbation Data, Regenerate of Data, distribution reconstruction, information privacy, random distortion, recovered data.

"From Perturbation Data, Regenerate of Data in Matlab"