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In this note, we describe a simple approach to obtain a differentiallyprivate algorithm for k-clustering with nearly the same multiplicative factoras any non-private counterpart at the cost of a large polynomial additiveerror. The approach is the combination of a simple geometric observationindependent of privacy consideration and any existing private algorithm with aconstant approximation.

A note on differentially private clustering with large additive error