A Simple Linear Space Data Structure for ANN with Application in Differential Privacy

Martin Aumüller, Fabrizio Boninsegna, Francesco Silvestri

Abstract: Locality Sensitive Filters are known for offering a quasi-linear space data structure with rigorous guarantees for the Approximate Near Neighbor search problem. Building on Locality Sensitive Filters, we derive a simple data structure for the Approximate Near Neighbor Counting problem under differential privacy. Moreover, we provide a simple analysis leveraging a connection with concomitant statistics and extreme value theory. Our approach achieves the same performance as the recent findings of Andoni et al. (NeurIPS 2023) but with a more straightforward method. As a side result, the paper provides a more compact description and analysis of Locality Sensitive Filters for Approximate Near Neighbor Search under inner product similarity, improving a previous result in Aum\"{u}ller et al. (TODS 2022).