Convergence analysis of the information matrix in Gaussian belief propagation

Gaussian belief propagation (BP) has been widely used for distributed estimation in large-scale networks such as the smart grid, communication networks, and social networks, where local measurements/observations are scattered over a wide geographical area. However, the convergence of Gaus- sian BP is still an open issue. In this paper, we consider the convergence of Gaussian BP, focusing in particular on the convergence of the information matrix. We show analytically that the exchanged message information matrix converges for arbitrary positive semidefinite initial value, and its dis- tance to the unique positive definite limit matrix decreases exponentially fast.Comment: arXiv admin note: substantial text overlap with arXiv:1611.0201

Peer-to-Peer Secure Multi-Party Numerical Computation Facing Malicious Adversaries

We propose an efficient framework for enabling secure multi-party numerical computations in a Peer-to-Peer network. This problem arises in a range of applications such as collaborative filtering, distributed computation of trust and reputation, monitoring and other tasks, where the computing nodes is expected to preserve the privacy of their inputs while performing a joint computation of a certain function. Although there is a rich literature in the field of distributed systems security concerning secure multi-party computation, in practice it is hard to deploy those methods in very large scale Peer-to-Peer networks. In this work, we try to bridge the gap between theoretical algorithms in the security domain, and a practical Peer-to-Peer deployment. We consider two security models. The first is the semi-honest model where peers correctly follow the protocol, but try to reveal private information. We provide three possible schemes for secure multi-party numerical computation for this model and identify a single light-weight scheme which outperforms the others. Using extensive simulation results over real Internet topologies, we demonstrate that our scheme is scalable to very large networks, with up to millions of nodes. The second model we consider is the malicious peers model, where peers can behave arbitrarily, deliberately trying to affect the results of the computation as well as compromising the privacy of other peers. For this model we provide a fourth scheme to defend the execution of the computation against the malicious peers. The proposed scheme has a higher complexity relative to the semi-honest model. Overall, we provide the Peer-to-Peer network designer a set of tools to choose from, based on the desired level of security.Comment: Submitted to Peer-to-Peer Networking and Applications Journal (PPNA) 200

From Internal to External: An Integrated Theoretical Framework for Open Innovation

The traditional resource-based view (RBV) accentuates the heterogeneous and imperfect mobile resources serve as key determinants of the competitiveness of organizations. However, social capital theory seems to be advocated leveraging the resources residing in the relationships among individuals to facilitate the organizational performances. The open innovation product, i.e., an Open Source Software (OSS) project, consists of a group of self-organizing individuals who collaboratively co-create an innovation. Much of our understandings of OSS an open innovation is based on studies that focused on the internal resources (i.e., on the co-workers predominantly) leaving little regards to the fact that such an open innovation organization functions in a larger community of projects and people. In this regard, it is imperative to jointly build upon the RBV and social capital theory to take a broader, embracing investigation of an open innovation, i.e. open source software (OSS), system to unveil how internal and external resources can facilitate the innovation legitimacy. In particular, the internal resources include the tangible resources, such as workforce and extent of contribution, and intangible resource like governance structure. The external resources include diverse forms of social capital, such as structural, cognitive, and relational social capital. This ongoing work proposed a theoretical framework to articulate how the interplay between internal and external resources can promote the innovation legitimacy in the OSS context

Peer-to-Peer Secure Multi-Party Numerical Computation

We propose an efficient framework for enabling secure multi-party numerical computations in a Peer-to-Peer network. This problem arises in a range of applications such as collaborative filtering, distributed computation of trust and reputation, monitoring and numerous other tasks, where the computing nodes would like to preserve the privacy of their inputs while performing a joint computation of a certain function. Although there is a rich literature in the field of distributed systems security concerning secure multi-party computation, in practice it is hard to deploy those methods in very large scale Peer-to-Peer networks. In this work, we examine several possible approaches and discuss their feasibility. Among the possible approaches, we identify a single approach which is both scalable and theoretically secure. An additional novel contribution is that we show how to compute the neighborhood based collaborative filtering, a state-of-the-art collaborative filtering algorithm, winner of the Netflix progress prize of the year 2007. Our solution computes this algorithm in a Peer-to-Peer network, using a privacy preserving computation, without loss of accuracy. Using extensive large scale simulations on top of real Internet topologies, we demonstrate the applicability of our approach. As far as we know, we are the first to implement such a large scale secure multi-party simulation of networks of millions of nodes and hundreds of millions of edges.Comment: 10 pages, 2 figures, appeared in the 8th IEEE Peer-to-Peer Computing, Aachen, Germany, Sept. 200

Upscaling in the sharing economy: insights from the UK

Aimed at academics, private businesses, investors and public sector bodies, this report develops a typology of upscaling models in the sharing economy across three key sectors: accommodation, transportation, and professional and personal services