Differential Privacy Via Wavelet Transforms

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Numerous organizations, like census bureaus and hospitals, maintain large collections of personal information (e.g., census data and medical records). Such data collections are of significant research value, and there is much benefit in making them publicly available. Nevertheless, as the data is sensitive in nature, proper measures must be taken to ensure that its publication does not endanger the privacy of the individuals that contributed the data. A canonical solution to this problem is to modify the data before releasing it to the public, such that the modification prevents inference of private information while retaining statistical characteristics of the data.