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There are many existing differentially private algorithms for releasinghistograms, i.e. counts with corresponding labels, in various settings. Ourfocus in this survey is to revisit some of the existing differentially privatealgorithms for releasing histograms over unknown domains, i.e. the labels ofthe counts that are to be released are not known beforehand. The main practicaladvantage of releasing histograms over an unknown domain is that the algorithmdoes not need to fill in missing labels because they are not present in theoriginal histogram but in a hypothetical neighboring dataset could appear inthe histogram. However, the challenge in designing differentially privatealgorithms for releasing histograms over an unknown domain is that someoutcomes can clearly show which input was used, clearly violating privacy. Thegoal then is to show that the differentiating outcomes occur with very lowprobability. We present a unified framework for the privacy analyses of severalexisting algorithms. Furthermore, our analysis uses approximate concentrateddifferential privacy from Bun and Steinke'16, which can improve the privacyloss parameters rather than using differential privacy directly, especiallywhen composing many of these algorithms together in an overall system.

A Unifying Privacy Analysis Framework for Unknown Domain Algorithms in Differential Privacy