195,802 research outputs found
Random Oracles in a Quantum World
The interest in post-quantum cryptography - classical systems that remain
secure in the presence of a quantum adversary - has generated elegant proposals
for new cryptosystems. Some of these systems are set in the random oracle model
and are proven secure relative to adversaries that have classical access to the
random oracle. We argue that to prove post-quantum security one needs to prove
security in the quantum-accessible random oracle model where the adversary can
query the random oracle with quantum states.
We begin by separating the classical and quantum-accessible random oracle
models by presenting a scheme that is secure when the adversary is given
classical access to the random oracle, but is insecure when the adversary can
make quantum oracle queries. We then set out to develop generic conditions
under which a classical random oracle proof implies security in the
quantum-accessible random oracle model. We introduce the concept of a
history-free reduction which is a category of classical random oracle
reductions that basically determine oracle answers independently of the history
of previous queries, and we prove that such reductions imply security in the
quantum model. We then show that certain post-quantum proposals, including ones
based on lattices, can be proven secure using history-free reductions and are
therefore post-quantum secure. We conclude with a rich set of open problems in
this area.Comment: 38 pages, v2: many substantial changes and extensions, merged with a
related paper by Boneh and Zhandr
Lower Bounds on the Oracle Complexity of Nonsmooth Convex Optimization via Information Theory
We present an information-theoretic approach to lower bound the oracle
complexity of nonsmooth black box convex optimization, unifying previous lower
bounding techniques by identifying a combinatorial problem, namely string
guessing, as a single source of hardness. As a measure of complexity we use
distributional oracle complexity, which subsumes randomized oracle complexity
as well as worst-case oracle complexity. We obtain strong lower bounds on
distributional oracle complexity for the box , as well as for the
-ball for (for both low-scale and large-scale regimes),
matching worst-case upper bounds, and hence we close the gap between
distributional complexity, and in particular, randomized complexity, and
worst-case complexity. Furthermore, the bounds remain essentially the same for
high-probability and bounded-error oracle complexity, and even for combination
of the two, i.e., bounded-error high-probability oracle complexity. This
considerably extends the applicability of known bounds
The Random Oracle Methodology, Revisited
We take a critical look at the relationship between the security of
cryptographic schemes in the Random Oracle Model, and the security of the
schemes that result from implementing the random oracle by so called
"cryptographic hash functions". The main result of this paper is a negative
one: There exist signature and encryption schemes that are secure in the Random
Oracle Model, but for which any implementation of the random oracle results in
insecure schemes.
In the process of devising the above schemes, we consider possible
definitions for the notion of a "good implementation" of a random oracle,
pointing out limitations and challenges.Comment: 31 page
On the Power of Conditional Samples in Distribution Testing
In this paper we define and examine the power of the {\em
conditional-sampling} oracle in the context of distribution-property testing.
The conditional-sampling oracle for a discrete distribution takes as
input a subset of the domain, and outputs a random sample drawn according to , conditioned on (and independently of all
prior samples). The conditional-sampling oracle is a natural generalization of
the ordinary sampling oracle in which always equals .
We show that with the conditional-sampling oracle, testing uniformity,
testing identity to a known distribution, and testing any label-invariant
property of distributions is easier than with the ordinary sampling oracle. On
the other hand, we also show that for some distribution properties the
sample-complexity remains near-maximal even with conditional sampling
An automated framework for software test oracle
Context: One of the important issues of software testing is to provide an automated test oracle. Test oracles are reliable sources of how the software under test must operate. In particular, they are used to evaluate the actual results that produced by the software. However, in order to generate an automated test oracle, oracle challenges need to be addressed. These challenges are output-domain generation, input domain to output domain mapping, and a comparator to decide on the accuracy of the actual outputs. Objective: This paper proposes an automated test oracle framework to address all of these challenges. Method: I/O Relationship Analysis is used to generate the output domain automatically and Multi-Networks Oracles based on artificial neural networks are introduced to handle the second challenge. The last challenge is addressed using an automated comparator that adjusts the oracle precision by defining the comparison tolerance. The proposed approach was evaluated using an industry strength case study, which was injected with some faults. The quality of the proposed oracle was measured by assessing its accuracy, precision, misclassification error and practicality. Mutation testing was considered to provide the evaluation framework by implementing two different versions of the case study: a Golden Version and a Mutated Version. Furthermore, a comparative study between the existing automated oracles and the proposed one is provided based on which challenges they can automate. Results: Results indicate that the proposed approach automated the oracle generation process 97% in this experiment. Accuracy of the proposed oracle was up to 98.26%, and the oracle detected up to 97.7% of the injected faults. Conclusion: Consequently, the results of the study highlight the practicality of the proposed oracle in addition to the automation it offers
A Linear-Size Logarithmic Stretch Path-Reporting Distance Oracle for General Graphs
In 2001 Thorup and Zwick devised a distance oracle, which given an -vertex
undirected graph and a parameter , has size . Upon a query
their oracle constructs a -approximate path between
and . The query time of the Thorup-Zwick's oracle is , and it was
subsequently improved to by Chechik. A major drawback of the oracle of
Thorup and Zwick is that its space is . Mendel and Naor
devised an oracle with space and stretch , but their
oracle can only report distance estimates and not actual paths. In this paper
we devise a path-reporting distance oracle with size , stretch
and query time , for an arbitrarily small .
In particular, our oracle can provide logarithmic stretch using linear size.
Another variant of our oracle has size , polylogarithmic
stretch, and query time .
For unweighted graphs we devise a distance oracle with multiplicative stretch
, additive stretch , for a function , space
, and query time , for an arbitrarily
small constant . The tradeoff between multiplicative stretch and
size in these oracles is far below girth conjecture threshold (which is stretch
and size ). Breaking the girth conjecture tradeoff is
achieved by exhibiting a tradeoff of different nature between additive stretch
and size . A similar type of tradeoff was exhibited by
a construction of -spanners due to Elkin and Peleg.
However, so far -spanners had no counterpart in the
distance oracles' world.
An important novel tool that we develop on the way to these results is a
{distance-preserving path-reporting oracle}
Development and Evaluation of the Oracle Intelligent Tutoring System (OITS)
This paper presents the design and development of intelligent tutoring system for teaching Oracle. The Oracle Intelligent Tutoring System (OITS) examined the power of a new methodology to supporting students in Oracle programming.
The system presents the topic of Introduction to Oracle with automatically generated problems for the students to solve. The system is dynamically adapted at run time to the studentās individual progress. An initial evaluation study was done to investigate the effect of using the intelligent tutoring system on the performance of students
Enumeration of Extractive Oracle Summaries
To analyze the limitations and the future directions of the extractive
summarization paradigm, this paper proposes an Integer Linear Programming (ILP)
formulation to obtain extractive oracle summaries in terms of ROUGE-N. We also
propose an algorithm that enumerates all of the oracle summaries for a set of
reference summaries to exploit F-measures that evaluate which system summaries
contain how many sentences that are extracted as an oracle summary. Our
experimental results obtained from Document Understanding Conference (DUC)
corpora demonstrated the following: (1) room still exists to improve the
performance of extractive summarization; (2) the F-measures derived from the
enumerated oracle summaries have significantly stronger correlations with human
judgment than those derived from single oracle summaries.Comment: 12 page
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