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Data Disclosure under Perfect Sample Privacy
2019
IEEE Transactions on Information Forensics and Security
Perfect data privacy seems to be in fundamental opposition to the economical and scientific opportunities associated with extensive data exchange. Defying this intuition, this paper develops a framework that allows the disclosure of collective properties of datasets without compromising the privacy of individual data samples. We present an algorithm to build an optimal disclosure strategy/mapping, and discuss it fundamental limits on finite and asymptotically large datasets. Furthermore, we
doi:10.1109/tifs.2019.2954652
fatcat:ogow44p4jnag5lahd2d44n4wj4