4,509 research outputs found
Estimating the cost of generic quantum pre-image attacks on SHA-2 and SHA-3
We investigate the cost of Grover's quantum search algorithm when used in the
context of pre-image attacks on the SHA-2 and SHA-3 families of hash functions.
Our cost model assumes that the attack is run on a surface code based
fault-tolerant quantum computer. Our estimates rely on a time-area metric that
costs the number of logical qubits times the depth of the circuit in units of
surface code cycles. As a surface code cycle involves a significant classical
processing stage, our cost estimates allow for crude, but direct, comparisons
of classical and quantum algorithms.
We exhibit a circuit for a pre-image attack on SHA-256 that is approximately
surface code cycles deep and requires approximately
logical qubits. This yields an overall cost of
logical-qubit-cycles. Likewise we exhibit a SHA3-256 circuit that is
approximately surface code cycles deep and requires approximately
logical qubits for a total cost of, again,
logical-qubit-cycles. Both attacks require on the order of queries in
a quantum black-box model, hence our results suggest that executing these
attacks may be as much as billion times more expensive than one would
expect from the simple query analysis.Comment: Same as the published version to appear in the Selected Areas of
Cryptography (SAC) 2016. Comments are welcome
Simple, compact and robust approximate string dictionary
This paper is concerned with practical implementations of approximate string
dictionaries that allow edit errors. In this problem, we have as input a
dictionary of strings of total length over an alphabet of size
. Given a bound and a pattern of length , a query has to
return all the strings of the dictionary which are at edit distance at most
from , where the edit distance between two strings and is defined as
the minimum-cost sequence of edit operations that transform into . The
cost of a sequence of operations is defined as the sum of the costs of the
operations involved in the sequence. In this paper, we assume that each of
these operations has unit cost and consider only three operations: deletion of
one character, insertion of one character and substitution of a character by
another. We present a practical implementation of the data structure we
recently proposed and which works only for one error. We extend the scheme to
. Our implementation has many desirable properties: it has a very
fast and space-efficient building algorithm. The dictionary data structure is
compact and has fast and robust query time. Finally our data structure is
simple to implement as it only uses basic techniques from the literature,
mainly hashing (linear probing and hash signatures) and succinct data
structures (bitvectors supporting rank queries).Comment: Accepted to a journal (19 pages, 2 figures
FSL-BM: Fuzzy Supervised Learning with Binary Meta-Feature for Classification
This paper introduces a novel real-time Fuzzy Supervised Learning with Binary
Meta-Feature (FSL-BM) for big data classification task. The study of real-time
algorithms addresses several major concerns, which are namely: accuracy, memory
consumption, and ability to stretch assumptions and time complexity. Attaining
a fast computational model providing fuzzy logic and supervised learning is one
of the main challenges in the machine learning. In this research paper, we
present FSL-BM algorithm as an efficient solution of supervised learning with
fuzzy logic processing using binary meta-feature representation using Hamming
Distance and Hash function to relax assumptions. While many studies focused on
reducing time complexity and increasing accuracy during the last decade, the
novel contribution of this proposed solution comes through integration of
Hamming Distance, Hash function, binary meta-features, binary classification to
provide real time supervised method. Hash Tables (HT) component gives a fast
access to existing indices; and therefore, the generation of new indices in a
constant time complexity, which supersedes existing fuzzy supervised algorithms
with better or comparable results. To summarize, the main contribution of this
technique for real-time Fuzzy Supervised Learning is to represent hypothesis
through binary input as meta-feature space and creating the Fuzzy Supervised
Hash table to train and validate model.Comment: FICC201
Cache-Oblivious Peeling of Random Hypergraphs
The computation of a peeling order in a randomly generated hypergraph is the
most time-consuming step in a number of constructions, such as perfect hashing
schemes, random -SAT solvers, error-correcting codes, and approximate set
encodings. While there exists a straightforward linear time algorithm, its poor
I/O performance makes it impractical for hypergraphs whose size exceeds the
available internal memory.
We show how to reduce the computation of a peeling order to a small number of
sequential scans and sorts, and analyze its I/O complexity in the
cache-oblivious model. The resulting algorithm requires
I/Os and time to peel a random hypergraph with edges.
We experimentally evaluate the performance of our implementation of this
algorithm in a real-world scenario by using the construction of minimal perfect
hash functions (MPHF) as our test case: our algorithm builds a MPHF of
billion keys in less than hours on a single machine. The resulting data
structure is both more space-efficient and faster than that obtained with the
current state-of-the-art MPHF construction for large-scale key sets
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