1,931,602 research outputs found
On Constant-Round Concurrent Zero-Knowledge from a Knowledge Assumption
In this work, we consider the long-standing open question of constructing
constant-round concurrent zero-knowledge protocols in the plain model.
Resolving this question is known to require non-black-box techniques.
We consider non-black-box techniques for zero-knowledge based on knowledge
assumptions, a line of thinking initiated by the work of Hada and Tanaka
(CRYPTO 1998). Prior to our work, it was not known whether knowledge
assumptions could be used for achieving security in the concurrent setting, due
to a number of significant limitations that we discuss here. Nevertheless, we
obtain the following results:
1. We obtain the first constant round concurrent zero-knowledge argument for
\textbf{NP} in the plain model based on a new variant of knowledge of exponent
assumption. Furthermore, our construction avoids the inefficiency inherent in
previous non-black-box techniques such that those of Barak (FOCS 2001); we
obtain our result through an efficient protocol compiler.
2. Unlike Hada and Tanaka, we do not require a knowledge assumption to argue
the soundness of our protocol. Instead, we use a discrete log like assumption,
which we call Diffie-Hellman Logarithm Assumption, to prove the soundness of
our protocol.
3. We give evidence that our new variant of knowledge of exponent assumption
is in fact plausible. In particular, we show that our assumption holds in the
generic group model.
4. Knowledge assumptions are especially delicate assumptions whose
plausibility may be hard to gauge. We give a novel framework to express
knowledge assumptions in a more flexible way, which may allow for formulation
of plausible assumptions and exploration of their impact and application in
cryptography.Comment: 30 pages, 3 figure
Zero-Shot Hashing via Transferring Supervised Knowledge
Hashing has shown its efficiency and effectiveness in facilitating
large-scale multimedia applications. Supervised knowledge e.g. semantic labels
or pair-wise relationship) associated to data is capable of significantly
improving the quality of hash codes and hash functions. However, confronted
with the rapid growth of newly-emerging concepts and multimedia data on the
Web, existing supervised hashing approaches may easily suffer from the scarcity
and validity of supervised information due to the expensive cost of manual
labelling. In this paper, we propose a novel hashing scheme, termed
\emph{zero-shot hashing} (ZSH), which compresses images of "unseen" categories
to binary codes with hash functions learned from limited training data of
"seen" categories. Specifically, we project independent data labels i.e.
0/1-form label vectors) into semantic embedding space, where semantic
relationships among all the labels can be precisely characterized and thus seen
supervised knowledge can be transferred to unseen classes. Moreover, in order
to cope with the semantic shift problem, we rotate the embedded space to more
suitably align the embedded semantics with the low-level visual feature space,
thereby alleviating the influence of semantic gap. In the meantime, to exert
positive effects on learning high-quality hash functions, we further propose to
preserve local structural property and discrete nature in binary codes.
Besides, we develop an efficient alternating algorithm to solve the ZSH model.
Extensive experiments conducted on various real-life datasets show the superior
zero-shot image retrieval performance of ZSH as compared to several
state-of-the-art hashing methods.Comment: 11 page
Increasing the power of the verifier in Quantum Zero Knowledge
In quantum zero knowledge, the assumption was made that the verifier is only
using unitary operations. Under this assumption, many nice properties have been
shown about quantum zero knowledge, including the fact that Honest-Verifier
Quantum Statistical Zero Knowledge (HVQSZK) is equal to Cheating-Verifier
Quantum Statistical Zero Knowledge (QSZK) (see [Wat02,Wat06]).
In this paper, we study what happens when we allow an honest verifier to flip
some coins in addition to using unitary operations. Flipping a coin is a
non-unitary operation but doesn't seem at first to enhance the cheating
possibilities of the verifier since a classical honest verifier can flip coins.
In this setting, we show an unexpected result: any classical Interactive Proof
has an Honest-Verifier Quantum Statistical Zero Knowledge proof with coins.
Note that in the classical case, honest verifier SZK is no more powerful than
SZK and hence it is not believed to contain even NP. On the other hand, in the
case of cheating verifiers, we show that Quantum Statistical Zero Knowledge
where the verifier applies any non-unitary operation is equal to Quantum
Zero-Knowledge where the verifier uses only unitaries.
One can think of our results in two complementary ways. If we would like to
use the honest verifier model as a means to study the general model by taking
advantage of their equivalence, then it is imperative to use the unitary
definition without coins, since with the general one this equivalence is most
probably not true. On the other hand, if we would like to use quantum zero
knowledge protocols in a cryptographic scenario where the honest-but-curious
model is sufficient, then adding the unitary constraint severely decreases the
power of quantum zero knowledge protocols.Comment: 17 pages, 0 figures, to appear in FSTTCS'0
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