  We investigate the problem of finding reverse nearest neighbors efficiently.
Although provably good solutions exist for this problem in low or fixed
dimensions, to this date the methods proposed in high dimensions are mostly
heuristic. We introduce a method that is both provably correct and efficient in
all dimensions, based on a reduction of the problem to one instance of
$\e$-nearest neighbor search plus a controlled number of instances of {\em
exhaustive $r$-\pleb}, a variant of {\em Point Location among Equal Balls}
where all the $r$-balls centered at the data points that contain the query
point are sought for, not just one. The former problem has been extensively
studied and elegantly solved in high dimensions using Locality-Sensitive
Hashing (LSH) techniques. By contrast, the latter problem has a complexity that
is still not fully understood. We revisit the analysis of the LSH scheme for
exhaustive $r$-\pleb using a somewhat refined notion of locality-sensitive
family of hash function, which brings out a meaningful output-sensitive term in
the complexity of the problem. Our analysis, combined with a non-isometric
lifting of the data, enables us to answer exhaustive $r$-\pleb queries (and
down the road reverse nearest neighbors queries) efficiently. Along the way, we
obtain a simple algorithm for answering exact nearest neighbor queries, whose
complexity is parametrized by some {\em condition number} measuring the
inherent difficulty of a given instance of the problem.
