1,421 research outputs found
Discrete Gaussian Measures and New Bounds of the Smoothing Parameter for Lattices
In this paper, we start with a discussion of discrete Gaussian measures on lattices.
Several results of Banaszczyk are analyzed, different approaches are suggested.
In the second part of the paper we prove two new bounds for the smoothing parameter of lattices.
Under the natural assumption that is suitably small, we obtain two estimations of the
smoothing parameter:
1.
2.
For a lattice of dimension ,
\[
\eta_{\varepsilon}({\cal L}) \le \sqrt{\frac{\ln \big(n-1+\frac{2n}{\varepsilon}\big)}{\pi}}\tilde{bl}({\cal L}).
\
Semantically Secure Lattice Codes for Compound MIMO Channels
We consider compound multi-input multi-output (MIMO) wiretap channels where
minimal channel state information at the transmitter (CSIT) is assumed. Code
construction is given for the special case of isotropic mutual information,
which serves as a conservative strategy for general cases. Using the flatness
factor for MIMO channels, we propose lattice codes universally achieving the
secrecy capacity of compound MIMO wiretap channels up to a constant gap
(measured in nats) that is equal to the number of transmit antennas. The
proposed approach improves upon existing works on secrecy coding for MIMO
wiretap channels from an error probability perspective, and establishes
information theoretic security (in fact semantic security). We also give an
algebraic construction to reduce the code design complexity, as well as the
decoding complexity of the legitimate receiver. Thanks to the algebraic
structures of number fields and division algebras, our code construction for
compound MIMO wiretap channels can be reduced to that for Gaussian wiretap
channels, up to some additional gap to secrecy capacity.Comment: IEEE Trans. Information Theory, to appea
On the Closest Vector Problem with a Distance Guarantee
We present a substantially more efficient variant, both in terms of running
time and size of preprocessing advice, of the algorithm by Liu, Lyubashevsky,
and Micciancio for solving CVPP (the preprocessing version of the Closest
Vector Problem, CVP) with a distance guarantee. For instance, for any , our algorithm finds the (unique) closest lattice point for any target
point whose distance from the lattice is at most times the length of
the shortest nonzero lattice vector, requires as preprocessing advice only vectors, and runs in
time .
As our second main contribution, we present reductions showing that it
suffices to solve CVP, both in its plain and preprocessing versions, when the
input target point is within some bounded distance of the lattice. The
reductions are based on ideas due to Kannan and a recent sparsification
technique due to Dadush and Kun. Combining our reductions with the LLM
algorithm gives an approximation factor of for search
CVPP, improving on the previous best of due to Lagarias, Lenstra,
and Schnorr. When combined with our improved algorithm we obtain, somewhat
surprisingly, that only O(n) vectors of preprocessing advice are sufficient to
solve CVPP with (the only slightly worse) approximation factor of O(n).Comment: An early version of the paper was titled "On Bounded Distance
Decoding and the Closest Vector Problem with Preprocessing". Conference on
Computational Complexity (2014
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