On solving distributed CS(O)Ps with privacy

Cryptographic protocols can enforce privacy in distributed computation of functions [Goldwasser'96] and are a competitor of the distributed constructive search techniques. [Goldreich'87,Chaum'87,Chaum'88,Ben-or'88] show how cryptographic protocols can be compiled from protocols/functions for honest agents. For some combinations of concepts of security and types of attacks, cryptographic protocols obtained this way can be safe. We discuss their application to constraint satisfaction (and optimization) problems. A first version of this report, with some notation problems, appears in the PhD thesis report [Silaghi'02]

Branch-and-Prune Search Strategies for Numerical Constraint Solving

When solving numerical constraints such as nonlinear equations and inequalities, solvers often exploit pruning techniques, which remove redundant value combinations from the domains of variables, at pruning steps. To find the complete solution set, most of these solvers alternate the pruning steps with branching steps, which split each problem into subproblems. This forms the so-called branch-and-prune framework, well known among the approaches for solving numerical constraints. The basic branch-and-prune search strategy that uses domain bisections in place of the branching steps is called the bisection search. In general, the bisection search works well in case (i) the solutions are isolated, but it can be improved further in case (ii) there are continuums of solutions (this often occurs when inequalities are involved). In this paper, we propose a new branch-and-prune search strategy along with several variants, which not only allow yielding better branching decisions in the latter case, but also work as well as the bisection search does in the former case. These new search algorithms enable us to employ various pruning techniques in the construction of inner and outer approximations of the solution set. Our experiments show that these algorithms speed up the solving process often by one order of magnitude or more when solving problems with continuums of solutions, while keeping the same performance as the bisection search when the solutions are isolated.Comment: 43 pages, 11 figure

Secure Multi-party Computation for selecting a solution according to a uniform distribution over all solutions of a general combinatorial problem

Secure simulations of arithmetic circuit and boolean circuit evaluations are known to save privacy while providing solutions to any probabilistic function over a field. The problem we want to solve is to select a random solution of a general combinatorial problem. Here we discuss how to specify the need of selecting a random solution of a general combinatorial problem, as a probabilistic function. Arithmetic circuits for finding the set of all solutions are simple to design [24]