Data holders, such as statistical institutions and financial organizations, have a very serious and demanding task when producing data for official and public use. It’s about controlling the risk of identity disclosure and protecting sensitive information when they communicate data-sets among themselves, to governmental agencies and to the public. One of the techniques applied is that of micro-...

Matsui's linear cryptanalysis for iterated block ciphers is generalized to an attack called . This attack exploits a weakness that can be described by an e ective partition-pair, i.e., a partition of the plaintext set and a partition of the next-to-last-round output set such that, for every key, the next-to-last-round outputs are non-uniformly distributed over the blocks of the second partition...

Keywords: Multivariate exponential power distributions Kurtosis Kullback–Leibler divergence Relative sensitivity a b s t r a c t The multivariate exponential power family is considered for n-dimensional random variables , Z, with a known partition Z ðY; XÞ of dimensions p and n À p, respectively, with interest focusing on the conditional distribution YjX. An infinitesimal variation of any param...

Random perturbation is a promising technique for privacy preserving data mining. It retains an original sensitive value with a certain probability and replaces it with a random value from the domain with the remaining probability. If the replacing value is chosen from a large domain, the retention probability must be small to protect privacy. For this reason, previous randomizationbased approac...

The most commonly used p-way partitioning method is recursive bisection (RB). It first divides a graph or a mesh into two equal-sized pieces, by a “good” bisection algorithm, and then recursively divides the two pieces. Ideally, we would like to use an optimal bisection algorithm. Because the optimal bisection problem that partitions a graph into two equal-sized subgraphs to minimize the number...

We present a solution to the problem of intensity image segmentation using Bayesian estimation in a multiscale set up. Our approach regards the number of regions, the data partition and the parameter vectors that describe the probability densities of the regions as unknowns. We compute their MAP estimates jointly by maximizing their joint posterior probability density given the data. Since the ...

A (set) partition of [n] = {1, 2, . . . , n} is a collection of disjoint subsets, called blocks, of [n] whose union is [n]. We will write a partition as a sequence of blocks (B1, B2, . . . , Bk) such that min(B1) < min(B2) < · · · < min(Bk), for instance, ({1, 4, 8}, {2, 5, 9}, {3}, {6, 7}). Let π = (B1, B2, . . . , Bk) be a partition of [n]. The canonical word of π is the word a1a2 · · · an, w...

A partition of the positive integers into sets A and B avoids a set S ⊂ N if no two distinct elements in the same part have a sum in S. If the partition is unique, S is uniquely avoidable. For any irrational α > 1, Chow and Long constructed a partition which avoids the numerators of all convergents to α, and conjectured that the set Sα which this partition avoided was uniquely avoidable. We pro...

The clique partitioning problem (CPP) can be formulated as follows. Given is a complete graph G = (V; E), with edge weights w ij 2 R for all fi; jg 2 E. A subset A E is called a clique partition if there is a partition of V into non-empty, disjoint sets V 1 S k p=1 ffi; jgji; j 2 V p g. The weight of such a clique partition A is deened as P fi;jg2A w ij. The problem is now to nd a clique partit...

and call it the essential weight of the partition μ. For our purpose it is convenient to ignore the parts equal to 1 in the partition because an element like (1, 2, 3) ∈ S3 is also an element of bigger symmetric groups. So we write μ = 22 , ..., nn for a partition and the corresponding class Cμ is a class of an arbitrary symmetric group Sn with n ≥ W (μ) depending on the context, i.e. C2 denote...