New Advances on Privacy-Preserving Policy Reconciliation
Entities define their own set of rules under which they are willing to collaborate, e.g., interact, share and exchange resources or information with others. Typically, these individual policies differ for different parties. Thus, collaboration requires the resolving of differences and reaching a consensus. This process is generally referred to as policy reconciliation. Current solutions for policy reconciliation do not take into account the privacy concerns of reconciliating parties. This paper addresses the problem of preserving privacy during policy reconciliation. We introduce new protocols that meet the privacy requirements of the organizations and allow parties to find a common policy rule which optimizes their individual preferences.
BoostReduce - A Framework For Strong Lattice Basis Reduction
In this paper, we propose a new generic reduction framework BoostReduce for strong lattice basis reduction. At the core of our new framework is an iterative method which uses a newly-developed algorithm for finding short lattice vectors and integrating them efficiently into an improved lattice basis. We present BoostBKZ as an instance of BoostReduce using the Block-Korkine-Zolotarev (BKZ) reduction. BoostBKZ is tailored to make effective use of modern computer architectures in that it takes advantage of multiple threads. Experimental results of BoostBKZ show a significant reduction in running time while maintaining the quality of the reduced lattice basis in comparison to the traditional BKZ reduction algorithm.