A note on differentially private clustering with large additive error
This work addresses privacy concerns in clustering for data analysts, but it is incremental as it builds on existing techniques with a trade-off in error.
The authors tackled the problem of achieving differential privacy in k-clustering by proposing a simple method that maintains a near-optimal multiplicative approximation factor but incurs a large polynomial additive error, combining a geometric observation with existing private algorithms.
In this note, we describe a simple approach to obtain a differentially private algorithm for k-clustering with nearly the same multiplicative factor as any non-private counterpart at the cost of a large polynomial additive error. The approach is the combination of a simple geometric observation independent of privacy consideration and any existing private algorithm with a constant approximation.