68,817 research outputs found
Distributed Function Computation with Confidentiality
A set of terminals observe correlated data and seek to compute functions of
the data using interactive public communication. At the same time, it is
required that the value of a private function of the data remains concealed
from an eavesdropper observing this communication. In general, the private
function and the functions computed by the nodes can be all different. We show
that a class of functions are securely computable if and only if the
conditional entropy of data given the value of private function is greater than
the least rate of interactive communication required for a related
multiterminal source-coding task. A single-letter formula is provided for this
rate in special cases.Comment: To Appear in IEEE JSAC: In-Network Computation: Exploring the
Fundamental Limits, April 201
ShakeMe: Key Generation From Shared Motion
Devices equipped with accelerometer sensors such as today's mobile devices
can make use of motion to exchange information. A typical example for shared
motion is shaking of two devices which are held together in one hand. Deriving
a shared secret (key) from shared motion, e.g. for device pairing, is an
obvious application for this. Only the keys need to be exchanged between the
peers and neither the motion data nor the features extracted from it. This
makes the pairing fast and easy. For this, each device generates an information
signal (key) independently of each other and, in order to pair, they should be
identical. The key is essentially derived by quantizing certain well
discriminative features extracted from the accelerometer data after an implicit
synchronization. In this paper, we aim at finding a small set of effective
features which enable a significantly simpler quantization procedure than the
prior art. Our tentative results with authentic accelerometer data show that
this is possible with a competent accuracy (%) and key strength (entropy
approximately bits).Comment: The paper is accepted to the 13th IEEE International Conference on
Pervasive Intelligence and Computing (PIComp-2015
Securely Outsourcing Large Scale Eigen Value Problem to Public Cloud
Cloud computing enables clients with limited computational power to
economically outsource their large scale computations to a public cloud with
huge computational power. Cloud has the massive storage, computational power
and software which can be used by clients for reducing their computational
overhead and storage limitation. But in case of outsourcing, privacy of
client's confidential data must be maintained. We have designed a protocol for
outsourcing large scale Eigen value problem to a malicious cloud which provides
input/output data security, result verifiability and client's efficiency. As
the direct computation method to find all eigenvectors is computationally
expensive for large dimensionality, we have used power iterative method for
finding the largest Eigen value and the corresponding Eigen vector of a matrix.
For protecting the privacy, some transformations are applied to the input
matrix to get encrypted matrix which is sent to the cloud and then decrypting
the result that is returned from the cloud for getting the correct solution of
Eigen value problem. We have also proposed result verification mechanism for
detecting robust cheating and provided theoretical analysis and experimental
result that describes high-efficiency, correctness, security and robust
cheating resistance of the proposed protocol
Linear Programming Relaxations for Goldreich's Generators over Non-Binary Alphabets
Goldreich suggested candidates of one-way functions and pseudorandom
generators included in . It is known that randomly generated
Goldreich's generator using -wise independent predicates with input
variables and output variables is not pseudorandom generator with
high probability for sufficiently large constant . Most of the previous
works assume that the alphabet is binary and use techniques available only for
the binary alphabet. In this paper, we deal with non-binary generalization of
Goldreich's generator and derives the tight threshold for linear programming
relaxation attack using local marginal polytope for randomly generated
Goldreich's generators. We assume that input
variables are known. In that case, we show that when , there is an
exact threshold
such
that for , the LP relaxation can determine
linearly many input variables of Goldreich's generator if
, and that the LP relaxation cannot determine
input variables of Goldreich's generator if
. This paper uses characterization of LP solutions by
combinatorial structures called stopping sets on a bipartite graph, which is
related to a simple algorithm called peeling algorithm.Comment: 14 pages, 1 figur
The Case for Quantum Key Distribution
Quantum key distribution (QKD) promises secure key agreement by using quantum
mechanical systems. We argue that QKD will be an important part of future
cryptographic infrastructures. It can provide long-term confidentiality for
encrypted information without reliance on computational assumptions. Although
QKD still requires authentication to prevent man-in-the-middle attacks, it can
make use of either information-theoretically secure symmetric key
authentication or computationally secure public key authentication: even when
using public key authentication, we argue that QKD still offers stronger
security than classical key agreement.Comment: 12 pages, 1 figure; to appear in proceedings of QuantumComm 2009
Workshop on Quantum and Classical Information Security; version 2 minor
content revision
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