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Improved Utility Analysis of Private CountSketch
[article]
2022
arXiv
pre-print
Sketching is an important tool for dealing with high-dimensional vectors that are sparse (or well-approximated by a sparse vector), especially useful in distributed, parallel, and streaming settings. It is known that sketches can be made differentially private by adding noise according to the sensitivity of the sketch, and this has been used in private analytics and federated learning settings. The post-processing property of differential privacy implies that all estimates computed from the
arXiv:2205.08397v2
fatcat:3vrl6z34fvgw3pikbjv6d3taqq