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Differential privacy is a promise, made by a data curator to a data subject: you will not be affected, adversely or otherwise, by allowing your data to be used in any study, no matter what other studies, data sets, or information from other sources is, or may become, available. This talk describes the productive role played by negative results in the formulation of differential privacy and the development of techniques for achieving it, concluding with a new negative result having implications related to participation in multiple, independently operated, differentially private databases.