Fair near neighbor search via sampling

Martin Aumuller, Sariel Har-Peled, Sepideh Mahabadi, Rasmus Pagh, Francesco Silvestri

Research output: Contribution to journalArticlepeer-review

Abstract

Similarity search is a fundamental algorithmic primitive, widely used in many computer science disciplines. Given a set of points S and a radius parameter r > 0, the rnear neighbor (r-NN) problem asks for a data structure that, given any query point q, returns a point p within distance at most r from q. In this paper, we study the r-NN problem in the light of individual fairness and providing equal opportunities: all points that are within distance r from the query should have the same probability to be returned. In the low-dimensional case, this problem was first studied by Hu, Qiao, and Tao (PODS 2014). Locality sensitive hashing (LSH), the theoretically strongest approach to similarity search in high dimensions, does not provide such a fairness guarantee.

Original languageEnglish (US)
Pages (from-to)42-49
Number of pages8
JournalSIGMOD Record
Volume50
Issue number1
DOIs
StatePublished - Mar 2021

ASJC Scopus subject areas

  • Software
  • Information Systems

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