41 research outputs found
Good Random Matrices over Finite Fields
The random matrix uniformly distributed over the set of all m-by-n matrices
over a finite field plays an important role in many branches of information
theory. In this paper a generalization of this random matrix, called k-good
random matrices, is studied. It is shown that a k-good random m-by-n matrix
with a distribution of minimum support size is uniformly distributed over a
maximum-rank-distance (MRD) code of minimum rank distance min{m,n}-k+1, and
vice versa. Further examples of k-good random matrices are derived from
homogeneous weights on matrix modules. Several applications of k-good random
matrices are given, establishing links with some well-known combinatorial
problems. Finally, the related combinatorial concept of a k-dense set of m-by-n
matrices is studied, identifying such sets as blocking sets with respect to
(m-k)-dimensional flats in a certain m-by-n matrix geometry and determining
their minimum size in special cases.Comment: 25 pages, publishe
Failure of the McEliece public-key cryptosystem under message-resend and related-message attack
Metrics generated by families of subspaces
A new family of metrics is introduced. Each of these is defined by a spanning set F of linear subspaces of a finite vector space. The norm of a vector is defined as the size of a minimal subset of F whose span contains this vector. Some examples and applications are presented. A-class of Varshamov-Gilbert bound based F-metrics is introduced. Connections with combinatorial metrics are discussed