61 research outputs found
Secure equality testing protocols in the two-party setting
Protocols for securely testing the equality of two encrypted integers are common building blocks for a number of proposals in the literature that aim for privacy preservation. Being used repeatedly in many cryptographic protocols, designing efficient equality testing protocols is important in terms of computation and communication overhead. In this work, we consider a scenario with two parties where party A has two integers encrypted using an additively homomorphic scheme and party B has the decryption key. Party A would like to obtain an encrypted bit that shows whether the integers are equal or not but nothing more. We propose three secure equality testing protocols, which are more efficient in terms of communication, computation or both compared to the existing work. To support our claims, we present experimental results, which show that our protocols achieve up to 99% computation-wise improvement compared to the state-of-the-art protocols in a fair experimental set-up
Secret Sharing Schemes with a large number of players from Toric Varieties
A general theory for constructing linear secret sharing schemes over a finite
field \Fq from toric varieties is introduced. The number of players can be as
large as for . We present general methods for obtaining
the reconstruction and privacy thresholds as well as conditions for
multiplication on the associated secret sharing schemes.
In particular we apply the method on certain toric surfaces. The main results
are ideal linear secret sharing schemes where the number of players can be as
large as . We determine bounds for the reconstruction and privacy
thresholds and conditions for strong multiplication using the cohomology and
the intersection theory on toric surfaces.Comment: 15 pages, 4 figures. arXiv admin note: text overlap with
arXiv:1203.454
Improvements on the k-center problem for uncertain data
In real applications, there are situations where we need to model some
problems based on uncertain data. This leads us to define an uncertain model
for some classical geometric optimization problems and propose algorithms to
solve them. In this paper, we study the -center problem, for uncertain
input. In our setting, each uncertain point is located independently from
other points in one of several possible locations in a metric space with metric , with specified probabilities
and the goal is to compute -centers that minimize the
following expected cost here
is the probability space of all realizations of given uncertain points and
In restricted assigned version of this problem, an assignment is given for any choice of centers and the
goal is to minimize In unrestricted version, the
assignment is not specified and the goal is to compute centers
and an assignment that minimize the above expected
cost.
We give several improved constant approximation factor algorithms for the
assigned versions of this problem in a Euclidean space and in a general metric
space. Our results significantly improve the results of \cite{guh} and
generalize the results of \cite{wang} to any dimension. Our approach is to
replace a certain center point for each uncertain point and study the
properties of these certain points. The proposed algorithms are efficient and
simple to implement
- β¦