2,875 research outputs found
Mechanisms for Outsourcing Computation via a Decentralized Market
As the number of personal computing and IoT devices grows rapidly, so does
the amount of computational power that is available at the edge. Since many of
these devices are often idle, there is a vast amount of computational power
that is currently untapped, and which could be used for outsourcing
computation. Existing solutions for harnessing this power, such as volunteer
computing (e.g., BOINC), are centralized platforms in which a single
organization or company can control participation and pricing. By contrast, an
open market of computational resources, where resource owners and resource
users trade directly with each other, could lead to greater participation and
more competitive pricing. To provide an open market, we introduce MODiCuM, a
decentralized system for outsourcing computation. MODiCuM deters participants
from misbehaving-which is a key problem in decentralized systems-by resolving
disputes via dedicated mediators and by imposing enforceable fines. However,
unlike other decentralized outsourcing solutions, MODiCuM minimizes
computational overhead since it does not require global trust in mediation
results. We provide analytical results proving that MODiCuM can deter
misbehavior, and we evaluate the overhead of MODiCuM using experimental results
based on an implementation of our platform
Raziel: Private and Verifiable Smart Contracts on Blockchains
Raziel combines secure multi-party computation and proof-carrying code to
provide privacy, correctness and verifiability guarantees for smart contracts
on blockchains. Effectively solving DAO and Gyges attacks, this paper describes
an implementation and presents examples to demonstrate its practical viability
(e.g., private and verifiable crowdfundings and investment funds).
Additionally, we show how to use Zero-Knowledge Proofs of Proofs (i.e.,
Proof-Carrying Code certificates) to prove the validity of smart contracts to
third parties before their execution without revealing anything else. Finally,
we show how miners could get rewarded for generating pre-processing data for
secure multi-party computation.Comment: Support: cothority/ByzCoin/OmniLedge
New Evidence on the Link between Technological Change and Employment: Extending the Neo-Classical Paradigm
A burgeoning literature on "skill-biased" technological change (SBTC) reveals that investment in information and communications technology (ICT) is associated with workforce reductions and an increase in the demand for highly educated workers. Based on extensions of the neo-classical paradigm, researchers have also come to realize that the implementation of a new technology is often accompanied by organizational change. Two edited volumes by Marco Vivarelli, Mario Pianta, Pascal Petit, and Luc Soete provide important new evidence on the policy implications of these trends. We review these volumes and other recent studies and also provide new evidence on the relationship between technological change and organizational change, based on a comprehensive dataset of Italian manufacturing firms.
CrowdCache: A Decentralized Game-Theoretic Framework for Mobile Edge Content Sharing
Mobile edge computing (MEC) is a promising solution for enhancing the user
experience, minimizing content delivery expenses, and reducing backhaul
traffic. In this paper, we propose a novel privacy-preserving decentralized
game-theoretic framework for resource crowdsourcing in MEC. Our framework
models the interactions between a content provider (CP) and multiple mobile
edge device users (MEDs) as a non-cooperative game, in which MEDs offer idle
storage resources for content caching in exchange for rewards. We introduce
efficient decentralized gradient play algorithms for Nash equilibrium (NE)
computation by exchanging local information among neighboring MEDs only, thus
preventing attackers from learning users' private information. The key
challenge in designing such algorithms is that communication among MEDs is not
fixed and is facilitated by a sequence of undirected time-varying graphs. Our
approach achieves linear convergence to the NE without imposing any assumptions
on the values of parameters in the local objective functions, such as requiring
strong monotonicity to be stronger than its dependence on other MEDs' actions,
which is commonly required in existing literature when the graph is directed
time-varying. Extensive simulations demonstrate the effectiveness of our
approach in achieving efficient resource outsourcing decisions while preserving
the privacy of the edge devices
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