2,010 research outputs found
Fast Cross-Polytope Locality-Sensitive Hashing
We provide a variant of cross-polytope locality sensitive hashing with
respect to angular distance which is provably optimal in asymptotic sensitivity
and enjoys hash computation time. Building on a recent
result (by Andoni, Indyk, Laarhoven, Razenshteyn, Schmidt, 2015), we show that
optimal asymptotic sensitivity for cross-polytope LSH is retained even when the
dense Gaussian matrix is replaced by a fast Johnson-Lindenstrauss transform
followed by discrete pseudo-rotation, reducing the hash computation time from
to . Moreover, our scheme achieves
the optimal rate of convergence for sensitivity. By incorporating a
low-randomness Johnson-Lindenstrauss transform, our scheme can be modified to
require only random bitsComment: 14 pages, 6 figure
Leftover Hashing Against Quantum Side Information
The Leftover Hash Lemma states that the output of a two-universal hash
function applied to an input with sufficiently high entropy is almost uniformly
random. In its standard formulation, the lemma refers to a notion of randomness
that is (usually implicitly) defined with respect to classical side
information. Here, we prove a (strictly) more general version of the Leftover
Hash Lemma that is valid even if side information is represented by the state
of a quantum system. Furthermore, our result applies to arbitrary delta-almost
two-universal families of hash functions. The generalized Leftover Hash Lemma
has applications in cryptography, e.g., for key agreement in the presence of an
adversary who is not restricted to classical information processing
Identification via Quantum Channels in the Presence of Prior Correlation and Feedback
Continuing our earlier work (quant-ph/0401060), we give two alternative
proofs of the result that a noiseless qubit channel has identification capacity
2: the first is direct by a "maximal code with random extension" argument, the
second is by showing that 1 bit of entanglement (which can be generated by
transmitting 1 qubit) and negligible (quantum) communication has identification
capacity 2.
This generalises a random hashing construction of Ahlswede and Dueck: that 1
shared random bit together with negligible communication has identification
capacity 1.
We then apply these results to prove capacity formulas for various quantum
feedback channels: passive classical feedback for quantum-classical channels, a
feedback model for classical-quantum channels, and "coherent feedback" for
general channels.Comment: 19 pages. Requires Rinton-P9x6.cls. v2 has some minor errors/typoes
corrected and the claims of remark 22 toned down (proofs are not so easy
after all). v3 has references to simultaneous ID coding removed: there were
necessary changes in quant-ph/0401060. v4 (final form) has minor correction
Sampling of min-entropy relative to quantum knowledge
Let X_1, ..., X_n be a sequence of n classical random variables and consider
a sample of r positions selected at random. Then, except with (exponentially in
r) small probability, the min-entropy of the sample is not smaller than,
roughly, a fraction r/n of the total min-entropy of all positions X_1, ...,
X_n, which is optimal. Here, we show that this statement, originally proven by
Vadhan [LNCS, vol. 2729, Springer, 2003] for the purely classical case, is
still true if the min-entropy is measured relative to a quantum system. Because
min-entropy quantifies the amount of randomness that can be extracted from a
given random variable, our result can be used to prove the soundness of locally
computable extractors in a context where side information might be
quantum-mechanical. In particular, it implies that key agreement in the
bounded-storage model (using a standard sample-and-hash protocol) is fully
secure against quantum adversaries, thus solving a long-standing open problem.Comment: 48 pages, late
Trenchcoat: Human-Computable Hashing Algorithms for Password Generation
The average user has between 90-130 online accounts, and around passwords are in use this year. Most people are terrible at
remembering "random" passwords, so they reuse or create similar passwords using
a combination of predictable words, numbers, and symbols. Previous
password-generation or management protocols have imposed so large a cognitive
load that users have abandoned them in favor of insecure yet simpler methods
(e.g., writing them down or reusing minor variants).
We describe a range of candidate human-computable "hash" functions suitable
for use as password generators - as long as the human (with minimal education
assumptions) keeps a single, easily-memorizable "master" secret - and rate them
by various metrics, including effective security.
These functions hash master-secrets with user accounts to produce sub-secrets
that can be used as passwords; s, takes a website
, produces a password , parameterized by master secret , which may or
may not be a string.
We exploit the unique configuration of each user's associative and
implicit memory (detailed in section 2) to ensure that sources of randomness
unique to each user are present in each master-secret . An adversary
cannot compute or verify efficiently since is unique to each
individual; in that sense, our hash function is similar to a physically
unclonable function. For the algorithms we propose, the user need only complete
primitive operations such as addition, spatial navigation or searching.
Critically, most of our methods are also accessible to neurodiverse, or
cognitively or physically differently-abled persons.
We present results from a survey (n=134 individuals) investigating real-world
usage of these methods and how people currently come up with their passwords,
we also survey 400 websites to collate current password advice
Impact of Feature Representation on Remote Sensing Image Retrieval
Remote sensing images are acquired using special platforms, sensors and are classified as aerial, multispectral and hyperspectral images. Multispectral and hyperspectral images are represented using large spectral vectors as compared to normal Red, Green, Blue (RGB) images. Hence, remote sensing image retrieval process from large archives is a challenging task. Remote sensing image retrieval mainly consist of feature representation as first step and finding out similar images to a query image as second step. Feature representation plays important part in the performance of remote sensing image retrieval process. Research work focuses on impact of feature representation of remote sensing images on the performance of remote sensing image retrieval. This study shows that more discriminative features of remote sensing images are needed to improve performance of remote sensing image retrieval process
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