12,300 research outputs found
Continuous Variable Quantum State Sharing via Quantum Disentanglement
Quantum state sharing is a protocol where perfect reconstruction of quantum
states is achieved with incomplete or partial information in a multi-partite
quantum networks. Quantum state sharing allows for secure communication in a
quantum network where partial information is lost or acquired by malicious
parties. This protocol utilizes entanglement for the secret state distribution,
and a class of "quantum disentangling" protocols for the state reconstruction.
We demonstrate a quantum state sharing protocol in which a tripartite entangled
state is used to encode and distribute a secret state to three players. Any two
of these players can collaborate to reconstruct the secret state, whilst
individual players obtain no information. We investigate a number of quantum
disentangling processes and experimentally demonstrate quantum state
reconstruction using two of these protocols. We experimentally measure a
fidelity, averaged over all reconstruction permutations, of F = 0.73. A result
achievable only by using quantum resources.Comment: Published, Phys. Rev. A 71, 033814 (2005) (7 figures, 11 pages
Security in Locally Repairable Storage
In this paper we extend the notion of {\em locally repairable} codes to {\em
secret sharing} schemes. The main problem that we consider is to find optimal
ways to distribute shares of a secret among a set of storage-nodes
(participants) such that the content of each node (share) can be recovered by
using contents of only few other nodes, and at the same time the secret can be
reconstructed by only some allowable subsets of nodes. As a special case, an
eavesdropper observing some set of specific nodes (such as less than certain
number of nodes) does not get any information. In other words, we propose to
study a locally repairable distributed storage system that is secure against a
{\em passive eavesdropper} that can observe some subsets of nodes.
We provide a number of results related to such systems including upper-bounds
and achievability results on the number of bits that can be securely stored
with these constraints.Comment: This paper has been accepted for publication in IEEE Transactions of
Information Theor
An Epitome of Multi Secret Sharing Schemes for General Access Structure
Secret sharing schemes are widely used now a days in various applications,
which need more security, trust and reliability. In secret sharing scheme, the
secret is divided among the participants and only authorized set of
participants can recover the secret by combining their shares. The authorized
set of participants are called access structure of the scheme. In Multi-Secret
Sharing Scheme (MSSS), k different secrets are distributed among the
participants, each one according to an access structure. Multi-secret sharing
schemes have been studied extensively by the cryptographic community. Number of
schemes are proposed for the threshold multi-secret sharing and multi-secret
sharing according to generalized access structure with various features. In
this survey we explore the important constructions of multi-secret sharing for
the generalized access structure with their merits and demerits. The features
like whether shares can be reused, participants can be enrolled or dis-enrolled
efficiently, whether shares have to modified in the renewal phase etc., are
considered for the evaluation
Cryptographic techniques used to provide integrity of digital content in long-term storage
The main objective of the project was to obtain advanced mathematical methods to guarantee the verification that a required level of data integrity is maintained in long-term storage. The secondary objective was to provide methods for the evaluation of data loss and recovery. Additionally, we have provided the following initial constraints for the problem: a limitation of additional storage space, a minimal threshold for desired level of data integrity and a defined probability of a single-bit corruption.
With regard to the main objective, the study group focused on the exploration methods based on hash values. It has been indicated that in the case of tight constraints, suggested by PWPW, it is not possible to provide any method based only on the hash values. This observation stems from the fact that the high probability of bit corruption leads to unacceptably large number of broken hashes, which in turn stands in contradiction with the limitation for additional storage space.
However, having loosened the initial constraints to some extent, the study group has proposed two methods that use only the hash values. The first method, based on a simple scheme of data subdivision in disjoint subsets, has been provided as a benchmark for other methods discussed in this report. The second method ("hypercube" method), introduced as a type of the wider class of clever-subdivision methods, is built on the concept of rewriting data-stream into a n-dimensional hypercube and calculating hash values for some particular (overlapping) sections of the cube.
We have obtained interesting results by combining hash value methods with error-correction techniques. The proposed framework, based on the BCH codes, appears to have promising properties, hence further research in this field is strongly recommended.
As a part of the report we have also presented features of secret sharing methods for the benefit of novel distributed data-storage scenarios. We have provided an overview of some interesting aspects of secret sharing techniques and several examples of possible applications
Distributed Hypothesis Testing with Social Learning and Symmetric Fusion
We study the utility of social learning in a distributed detection model with
agents sharing the same goal: a collective decision that optimizes an agreed
upon criterion. We show that social learning is helpful in some cases but is
provably futile (and thus essentially a distraction) in other cases.
Specifically, we consider Bayesian binary hypothesis testing performed by a
distributed detection and fusion system, where all decision-making agents have
binary votes that carry equal weight. Decision-making agents in the team
sequentially make local decisions based on their own private signals and all
precedent local decisions. It is shown that the optimal decision rule is not
affected by precedent local decisions when all agents observe conditionally
independent and identically distributed private signals. Perfect Bayesian
reasoning will cancel out all effects of social learning. When the agents
observe private signals with different signal-to-noise ratios, social learning
is again futile if the team decision is only approved by unanimity. Otherwise,
social learning can strictly improve the team performance. Furthermore, the
order in which agents make their decisions affects the team decision.Comment: 10 pages, 7 figure
Lagrange Coded Computing: Optimal Design for Resiliency, Security and Privacy
We consider a scenario involving computations over a massive dataset stored
distributedly across multiple workers, which is at the core of distributed
learning algorithms. We propose Lagrange Coded Computing (LCC), a new framework
to simultaneously provide (1) resiliency against stragglers that may prolong
computations; (2) security against Byzantine (or malicious) workers that
deliberately modify the computation for their benefit; and (3)
(information-theoretic) privacy of the dataset amidst possible collusion of
workers. LCC, which leverages the well-known Lagrange polynomial to create
computation redundancy in a novel coded form across workers, can be applied to
any computation scenario in which the function of interest is an arbitrary
multivariate polynomial of the input dataset, hence covering many computations
of interest in machine learning. LCC significantly generalizes prior works to
go beyond linear computations. It also enables secure and private computing in
distributed settings, improving the computation and communication efficiency of
the state-of-the-art. Furthermore, we prove the optimality of LCC by showing
that it achieves the optimal tradeoff between resiliency, security, and
privacy, i.e., in terms of tolerating the maximum number of stragglers and
adversaries, and providing data privacy against the maximum number of colluding
workers. Finally, we show via experiments on Amazon EC2 that LCC speeds up the
conventional uncoded implementation of distributed least-squares linear
regression by up to , and also achieves a
- speedup over the state-of-the-art straggler
mitigation strategies
Usefulness of classical communication for local cloning of entangled states
We solve the problem of the optimal cloning of pure entangled two-qubit
states with a fixed degree of entanglement using local operations and classical
communication. We show, that amazingly, classical communication between the
parties can improve the fidelity of local cloning if and only if the initial
entanglement is higher than a certain critical value. It is completely useless
for weakly entangled states. We also show that bound entangled states with
positive partial transpose are not useful as a resource to improve the best
local cloning fidelity.Comment: 6 pages, RevTeX4, 2 figures, published versio
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