Junliang Huang

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We consider accurately answering smooth queries while preserving differential privacy. A query is said to be K-smooth if it is specified by a function defined on [−1, 1] d whose partial derivatives up to order K are all bounded. We develop an ǫ-differentially private mechanism for the class of K-smooth queries. The major advantage of the algorithm is that(More)
In the past few years, differential privacy has become a standard concept in the area of privacy. One of the most important problems in this field is to answer queries while preserving differential privacy. In spite of extensive studies, most existing work on differentially private query answering assumes the data are discrete (i.e., in {0, 1} d) and(More)
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