Malicious Security for Sparse Private Histograms

ePrint Report: Malicious Security for Sparse Private Histograms

Lennart Braun, Adrià Gascón, Mariana Raykova, Phillipp Schoppmann, Karn Seth

We present a construction for secure computation of differentially private sparse histograms that aggregates the inputs from a large number of clients. Each client contributes a value to the aggregate at a specific index. We focus on the case where the set of possible indices is superpolynomially large. Hence, the resulting histogram will be sparse, i.e., most entries will have the value …

client clients computation construction eprint report focus inputs large malicious private report secure computation security value