Dynamic Diameter in High-Dimensions against Adaptive Adversary and Beyond

TL;DR

This paper introduces efficient algorithms for maintaining approximate diameter and clustering in high-dimensional dynamic datasets, resilient to adaptive adversaries, with significant improvements in update times.

Contribution

It presents the first fully-dynamic, robust algorithm for high-dimensional diameter approximation and an improved adaptive clustering algorithm with better update efficiency.

Findings

Maintains 2-approximate diameter with poly(d, log n) update time.

Provides an improved (4+ε)-approximate k-center clustering algorithm.

Achieves robustness against adaptive adversaries in dynamic high-dimensional settings.

Abstract

In this paper, we study the fundamental problems of maintaining the diameter and a $k$-center clustering of a dynamic point set $P \subset \mathbb{R}^d$, where points may be inserted or deleted over time and the ambient dimension $d$ is not constant and may be high. Our focus is on designing algorithms that remain effective even in the presence of an adaptive adversary -- an adversary that, at any time $t$, knows the entire history of the algorithm's outputs as well as all the random bits used by the algorithm up to that point. We present a fully dynamic algorithm that maintains a $2$-approximate diameter with a worst-case update time of $\text{poly}(d, \log n)$, where $n$ is the length of the stream. Our result is achieved by identifying a robust representative of the dataset that requires infrequent updates, combined with a careful deamortization. To the best of our knowledge, this is the first efficient fully-dynamic algorithm for diameter in high dimensions that simultaneously achieves a 2-approximation guarantee and robustness against an adaptive adversary. We also give an improved dynamic $(4+\epsilon)$-approximation algorithm for the $k$-center problem, also resilient to an adaptive adversary. Our clustering algorithm achieves an amortized update time of $k^{2.5} d \cdot \text{poly}(\epsilon^{-1}, \log n)$, improving upon the amortized update time of $k^6 d \cdot \text{poly}(\epsilon^{-1}, \log n)$ by Biabani et al. [NeurIPS'24].