Sample and Threshold Differential Privacy: Histograms and applications

Federated analytics seeks to compute accurate statistics from data distributed across users' devices while providing a suitable privacy guarantee and being practically feasible to implement and scale. In this paper, we show how a strong $(ε, δ)$-Differential Privacy (DP) guarantee can be achieved for the fundamental problem of histogram generation in a federated setting, via a highly practical sampling-based procedure that does not add noise to disclosed data. Given the ubiquity of sampling in practice, we thus obtain a DP guarantee almost for free, avoid over-estimating histogram counts, and allow easy reasoning about how privacy guarantees may obscure minorities and outliers. Using such histograms, related problems such as heavy hitters and quantiles can be answered with provable error and privacy guarantees. Experimental results show that our sample-and-threshold approach is accurate and scalable.

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