4,185 research outputs found
A linear lower bound for incrementing a space-optimal integer representation in the bit-probe model
We present the first linear lower bound for the number of bits required to be
accessed in the worst case to increment an integer in an arbitrary space-
optimal binary representation. The best previously known lower bound was
logarithmic. It is known that a logarithmic number of read bits in the worst
case is enough to increment some of the integer representations that use one
bit of redundancy, therefore we show an exponential gap between space-optimal
and redundant counters.
Our proof is based on considering the increment procedure for a space optimal
counter as a permutation and calculating its parity. For every space optimal
counter, the permutation must be odd, and implementing an odd permutation
requires reading at least half the bits in the worst case. The combination of
these two observations explains why the worst-case space-optimal problem is
substantially different from both average-case approach with constant expected
number of reads and almost space optimal representations with logarithmic
number of reads in the worst case.Comment: 12 pages, 4 figure
Towards the Efficient Generation of Gray Codes in the Bitprobe Model
We examine the problem of representing integers modulo L so that both increment and decrement operations can be performed efficiently. This problem is studied in the bitprobe model, where the complexity of the underlying problem is measured by the number of bit operations performed on the data structure. In this thesis, we will primarily be interested in constructing space-optimal data structures. That is, we would like to use exactly n bits to represent integers modulo 2^n. Brodal et al. gave such a data structure, which requires n-1 bit reads and 3 bit writes, in the worst case, to perform increment and decrement operations We provide several improvements to their data structure. First, we give a data structure that requires n-1 bit reads and 2 bit writes, in the worst case, to perform increment and decrement operations. Then, we refine this result to obtain a data structure that requires n-1 bit reads and a single bit write to perform both operations. This disproves the conjecture that, when a space-optimal data structure uses only 1 bit write to perform these operations, then every bit in the data structure must be inspected in the worst case
Wavelet Trees Meet Suffix Trees
We present an improved wavelet tree construction algorithm and discuss its
applications to a number of rank/select problems for integer keys and strings.
Given a string of length n over an alphabet of size , our
method builds the wavelet tree in time,
improving upon the state-of-the-art algorithm by a factor of .
As a consequence, given an array of n integers we can construct in time a data structure consisting of machine words and
capable of answering rank/select queries for the subranges of the array in
time. This is a -factor improvement in
query time compared to Chan and P\u{a}tra\c{s}cu and a -factor
improvement in construction time compared to Brodal et al.
Next, we switch to stringological context and propose a novel notion of
wavelet suffix trees. For a string w of length n, this data structure occupies
words, takes time to construct, and simultaneously
captures the combinatorial structure of substrings of w while enabling
efficient top-down traversal and binary search. In particular, with a wavelet
suffix tree we are able to answer in time the following two
natural analogues of rank/select queries for suffixes of substrings: for
substrings x and y of w count the number of suffixes of x that are
lexicographically smaller than y, and for a substring x of w and an integer k,
find the k-th lexicographically smallest suffix of x.
We further show that wavelet suffix trees allow to compute a
run-length-encoded Burrows-Wheeler transform of a substring x of w in time, where s denotes the length of the resulting run-length encoding.
This answers a question by Cormode and Muthukrishnan, who considered an
analogous problem for Lempel-Ziv compression.Comment: 33 pages, 5 figures; preliminary version published at SODA 201
Homomorphic Encryption for Speaker Recognition: Protection of Biometric Templates and Vendor Model Parameters
Data privacy is crucial when dealing with biometric data. Accounting for the
latest European data privacy regulation and payment service directive,
biometric template protection is essential for any commercial application.
Ensuring unlinkability across biometric service operators, irreversibility of
leaked encrypted templates, and renewability of e.g., voice models following
the i-vector paradigm, biometric voice-based systems are prepared for the
latest EU data privacy legislation. Employing Paillier cryptosystems, Euclidean
and cosine comparators are known to ensure data privacy demands, without loss
of discrimination nor calibration performance. Bridging gaps from template
protection to speaker recognition, two architectures are proposed for the
two-covariance comparator, serving as a generative model in this study. The
first architecture preserves privacy of biometric data capture subjects. In the
second architecture, model parameters of the comparator are encrypted as well,
such that biometric service providers can supply the same comparison modules
employing different key pairs to multiple biometric service operators. An
experimental proof-of-concept and complexity analysis is carried out on the
data from the 2013-2014 NIST i-vector machine learning challenge
Data Structuring Problems in the Bit Probe Model
We study two data structuring problems under the bit probe model: the dynamic predecessor problem and integer representation in a manner supporting basic updates in as few bit operations as possible. The model of computation considered in this paper is the bit probe model. In this model, the complexity measure counts only the bitwise accesses to the data structure. The model ignores the cost of computation. As a result, the bit probe complexity of a data structuring problem can be considered as a fundamental measure of the problem. Lower bounds derived by this model are valid as lower bounds for any realistic, sequential model of computation. Furthermore, some of the problems are more suitable for study in this model as they can be solved using less than bit probes where is the size of a computer word.
The predecessor problem is one of the fundamental problems in computer science with numerous applications and has been studied for several decades. We study the colored predecessor problem, a variation of the predecessor problem, in which each element is associated with a symbol from a finite alphabet or color. The problem is to store a subset of size from a finite universe so that to support efficient insertion, deletion and queries to determine the color of the largest value in which is not larger than for a given We present a data structure for the problem that requires bit probes for the query and bit probes for the update operations, where is the universe size and is positive constant. We also show that the results on the colored predecessor problem can be used to solve some other related problems such as existential range query, dynamic prefix sum, segment representative, connectivity problems, etc.
The second structure considered is for integer representation. We examine the problem of integer representation in a nearly minimal number of bits so that increment and decrement (and indeed addition and subtraction) can be performed using few bit inspections and fewer bit changes. In particular, we prove a new lower bound of for the increment and decrement operation, where is the minimum number of bits required to represent the number. We present several efficient data structures to represent integers that use a logarithmic number of bit inspections and a constant number of bit changes per operation
- …