Privacy Preserving synthesized data releasing via generative adversarial networks

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Privacy Preserving synthesized data releasing via generative adversarial networks******The recent Facebook privacy scandal involving a London-based data-mining firm on misusing Facebook information of tens of millions of users highlights again the privacy concern over collecting, storing, and using sensitive data for data analysis. The traditional data sanitization technique addresses privacy concerns by masking sensitive information in true data and releasing the masked version. This approach makes certain assumptions on what is sensitive information and how the data will be used. Often these information are not available, so excessive data sanitization is necessary, which destroys data utility for potential analyses. The objective of this proposed research is to investigate the alternative of releasing synthesized data generated from true data by preserving the distributional characteristics of true data, instead of releasing actual individuals' records. The key is how to preserve distributional characteristics and how to ensure that doing so does not disclose sensitive information about individuals. ******The recent development of Generative Adversarial Networks (GANs) in machine learning and deep neural networks opens up new possibilities to address the above problem. GANs are a system of two neural networks contesting with each other in a zero-sum game framework. The generative network learns to map from a latent space to a particular data distribution of interest, while the discriminative network discriminates between instances from the true data distribution and candidates produced by the generative network. Both networks improve their methods until the synthesized instances are indistinguishable from the genuine ones, i.e., preserve the distributional characteristics of true data. To address privacy concerns, previous works, mainly from image generation, added random noises to perturb the gradient during stochastic gradient descent in the training of GANs. Since the perturbed gradient adversely affects the convergence rate and the utility of solutions, only weak privacy settings were evaluated because of poor utility. The proposed research will investigate alternatives ways of adding noises that could better preserve both privacy and utility, and evaluate data utility in a broad range of domains. One application of special interests to us is releasing medical and healthcare data to researchers, thanks to our access to true data and expertise in this domain. The significance of this research is that the data holder does not have to be concerned with data privacy because no true data is released and the released data meets a strong privacy guarantee; on the other hand, the data analyst will get nearly the same result as if true data were analyzed. This work will contribute to the practice of privacy preservation and the encouragement of data sharing for the benefits of data analysis. *****