3,736 research outputs found
Succinct Representations of Permutations and Functions
We investigate the problem of succinctly representing an arbitrary
permutation, \pi, on {0,...,n-1} so that \pi^k(i) can be computed quickly for
any i and any (positive or negative) integer power k. A representation taking
(1+\epsilon) n lg n + O(1) bits suffices to compute arbitrary powers in
constant time, for any positive constant \epsilon <= 1. A representation taking
the optimal \ceil{\lg n!} + o(n) bits can be used to compute arbitrary powers
in O(lg n / lg lg n) time.
We then consider the more general problem of succinctly representing an
arbitrary function, f: [n] \rightarrow [n] so that f^k(i) can be computed
quickly for any i and any integer power k. We give a representation that takes
(1+\epsilon) n lg n + O(1) bits, for any positive constant \epsilon <= 1, and
computes arbitrary positive powers in constant time. It can also be used to
compute f^k(i), for any negative integer k, in optimal O(1+|f^k(i)|) time.
We place emphasis on the redundancy, or the space beyond the
information-theoretic lower bound that the data structure uses in order to
support operations efficiently. A number of lower bounds have recently been
shown on the redundancy of data structures. These lower bounds confirm the
space-time optimality of some of our solutions. Furthermore, the redundancy of
one of our structures "surpasses" a recent lower bound by Golynski [Golynski,
SODA 2009], thus demonstrating the limitations of this lower bound.Comment: Preliminary versions of these results have appeared in the
Proceedings of ICALP 2003 and 2004. However, all results in this version are
improved over the earlier conference versio
Compressed Representations of Permutations, and Applications
We explore various techniques to compress a permutation over n
integers, taking advantage of ordered subsequences in , while supporting
its application (i) and the application of its inverse in
small time. Our compression schemes yield several interesting byproducts, in
many cases matching, improving or extending the best existing results on
applications such as the encoding of a permutation in order to support iterated
applications of it, of integer functions, and of inverted lists and
suffix arrays
LRM-Trees: Compressed Indices, Adaptive Sorting, and Compressed Permutations
LRM-Trees are an elegant way to partition a sequence of values into sorted
consecutive blocks, and to express the relative position of the first element
of each block within a previous block. They were used to encode ordinal trees
and to index integer arrays in order to support range minimum queries on them.
We describe how they yield many other convenient results in a variety of areas,
from data structures to algorithms: some compressed succinct indices for range
minimum queries; a new adaptive sorting algorithm; and a compressed succinct
data structure for permutations supporting direct and indirect application in
time all the shortest as the permutation is compressible.Comment: 13 pages, 1 figur
Bloom Filters in Adversarial Environments
Many efficient data structures use randomness, allowing them to improve upon
deterministic ones. Usually, their efficiency and correctness are analyzed
using probabilistic tools under the assumption that the inputs and queries are
independent of the internal randomness of the data structure. In this work, we
consider data structures in a more robust model, which we call the adversarial
model. Roughly speaking, this model allows an adversary to choose inputs and
queries adaptively according to previous responses. Specifically, we consider a
data structure known as "Bloom filter" and prove a tight connection between
Bloom filters in this model and cryptography.
A Bloom filter represents a set of elements approximately, by using fewer
bits than a precise representation. The price for succinctness is allowing some
errors: for any it should always answer `Yes', and for any it should answer `Yes' only with small probability.
In the adversarial model, we consider both efficient adversaries (that run in
polynomial time) and computationally unbounded adversaries that are only
bounded in the number of queries they can make. For computationally bounded
adversaries, we show that non-trivial (memory-wise) Bloom filters exist if and
only if one-way functions exist. For unbounded adversaries we show that there
exists a Bloom filter for sets of size and error , that is
secure against queries and uses only
bits of memory. In comparison, is the best
possible under a non-adaptive adversary
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