15 research outputs found
An Improved Approximate Consensus Algorithm in the Presence of Mobile Faults
This paper explores the problem of reaching approximate consensus in
synchronous point-to-point networks, where each pair of nodes is able to
communicate with each other directly and reliably. We consider the mobile
Byzantine fault model proposed by Garay '94 -- in the model, an omniscient
adversary can corrupt up to nodes in each round, and at the beginning of
each round, faults may "move" in the system (i.e., different sets of nodes may
become faulty in different rounds). Recent work by Bonomi et al. '16 proposed a
simple iterative approximate consensus algorithm which requires at least
nodes. This paper proposes a novel technique of using "confession" (a mechanism
to allow others to ignore past behavior) and a variant of reliable broadcast to
improve the fault-tolerance level. In particular, we present an approximate
consensus algorithm that requires only nodes, an
improvement over the state-of-the-art algorithms.
Moreover, we also show that the proposed algorithm is optimal within a family
of round-based algorithms
A Mechanism for Fair Distribution of Resources without Payments
We design a mechanism for Fair and Efficient Distribution of Resources
(FEDoR) in the presence of strategic agents. We consider a multiple-instances,
Bayesian setting, where in each round the preference of an agent over the set
of resources is a private information. We assume that in each of r rounds n
agents are competing for k non-identical indivisible goods, (n > k). In each
round the strategic agents declare how much they value receiving any of the
goods in the specific round. The agent declaring the highest valuation receives
the good with the highest value, the agent with the second highest valuation
receives the second highest valued good, etc. Hence we assume a decision
function that assigns goods to agents based on their valuations. The novelty of
the mechanism is that no payment scheme is required to achieve truthfulness in
a setting with rational/strategic agents. The FEDoR mechanism takes advantage
of the repeated nature of the framework, and through a statistical test is able
to punish the misreporting agents and be fair, truthful, and socially
efficient. FEDoR is fair in the sense that, in expectation over the course of
the rounds, all agents will receive the same good the same amount of times.
FEDoR is an eligible candidate for applications that require fair distribution
of resources over time. For example, equal share of bandwidth for nodes through
the same point of access. But further on, FEDoR can be applied in less trivial
settings like sponsored search, where payment is necessary and can be given in
the form of a flat participation fee. To this extent we perform a comparison
with traditional mechanisms applied to sponsored search, presenting the
advantage of FEDoR
Wendy, the Good Little Fairness Widget
The advent of decentralized trading markets introduces a number of new
challenges for consensus protocols. In addition to the `usual' attacks -- a
subset of the validators trying to prevent disagreement -- there is now the
possibility of financial fraud, which can abuse properties not normally
considered critical in consensus protocols. We investigate the issues of
attackers manipulating or exploiting the order in which transactions are
scheduled in the blockchain. More concretely, we look into relative order
fairness, i.e., ways we can assure that the relative order of transactions is
fair. We show that one of the more intuitive definitions of fairness is
impossible to achieve. We then present Wendy, a group of low overhead protocols
that can implement different concepts of fairness. Wendy acts as an additional
widget for an existing blockchain, and is largely agnostic to the underlying
blockchain and its security assumptions. Furthermore, it is possible to apply a
the protocol only for a subset of the transactions, and thus run several
independent fair markets on the same chain
Boosting the Efficiency of Byzantine-tolerant Reliable Communication
Reliable communication is a fundamental primitive in distributed systems prone to Byzantine (i.e. arbitrary, and possibly malicious) failures to guarantee integrity, delivery and authorship of messages exchanged between processes. Its practical adoption strongly depends on the system assumptions. One of the most general (and hence versatile) such hypothesis assumes a set of processes interconnected through an unknown communication network of reliable and authenticated links, and an upper bound on the number of Byzantine faulty processes that may be present in the system, known to all participants. To this date, implementing a reliable communication service in such an environment may be expensive, both in terms of message complexity and computational complexity, unless the topology of the network is known. The target of this work is to combine the Byzantine fault-tolerant topol-ogy reconstruction with a reliable communication primitive, aiming to boost the efficiency of the reliable communication service component after an initial (expensive) phase where the topology is partially reconstructed. We characterize the sets of assumptions that make our objective achievable, and we propose a solution that, after an initialization phase, guarantees reliable communication with optimal message complexity and optimal delivery complexity
Achieving reliability and fairness in online task computing environments
Mención Internacional en el título de doctorWe consider online task computing environments such as volunteer computing platforms running
on BOINC (e.g., SETI@home) and crowdsourcing platforms such as Amazon Mechanical
Turk. We model the computations as an Internet-based task computing system under the masterworker
paradigm. A master entity sends tasks across the Internet, to worker entities willing to
perform a computational task. Workers execute the tasks, and report back the results, completing
the computational round. Unfortunately, workers are untrustworthy and might report an incorrect
result. Thus, the first research question we answer in this work is how to design a reliable masterworker
task computing system. We capture the workers’ behavior through two realistic models:
(1) the “error probability model” which assumes the presence of altruistic workers willing to
provide correct results and the presence of troll workers aiming at providing random incorrect
results. Both types of workers suffer from an error probability altering their intended response.
(2) The “rationality model” which assumes the presence of altruistic workers, always reporting
a correct result, the presence of malicious workers always reporting an incorrect result, and the
presence of rational workers following a strategy that will maximize their utility (benefit). The
rational workers can choose among two strategies: either be honest and report a correct result,
or cheat and report an incorrect result. Our two modeling assumptions on the workers’ behavior
are supported by an experimental evaluation we have performed on Amazon Mechanical Turk.
Given the error probability model, we evaluate two reliability techniques: (1) “voting” and (2)
“auditing” in terms of task assignments required and time invested for computing correctly a set
of tasks with high probability. Considering the rationality model, we take an evolutionary game
theoretic approach and we design mechanisms that eventually achieve a reliable computational
platform where the master receives the correct task result with probability one and with minimal
auditing cost. The designed mechanisms provide incentives to the rational workers, reinforcing
their strategy to a correct behavior, while they are complemented by four reputation schemes that
cope with malice. Finally, we also design a mechanism that deals with unresponsive workers by
keeping a reputation related to the workers’ response rate. The designed mechanism selects the
most reliable and active workers in each computational round. Simulations, among other, depict
the trade-off between the master’s cost and the time the system needs to reach a state where
the master always receives the correct task result. The second research question we answer in
this work concerns the fair and efficient distribution of workers among the masters over multiple computational rounds. Masters with similar tasks are competing for the same set of workers at
each computational round. Workers must be assigned to the masters in a fair manner; when the
master values a worker’s contribution the most. We consider that a master might have a strategic
behavior, declaring a dishonest valuation on a worker in each round, in an attempt to increase its
benefit. This strategic behavior from the side of the masters might lead to unfair and inefficient assignments
of workers. Applying renown auction mechanisms to solve the problem at hand can be
infeasible since monetary payments are required on the side of the masters. Hence, we present an
alternative mechanism for fair and efficient distribution of the workers in the presence of strategic
masters, without the use of monetary incentives. We show analytically that our designed mechanism
guarantees fairness, is socially efficient, and is truthful. Simulations favourably compare
our designed mechanism with two benchmark auction mechanisms.This work has been supported by IMDEA Networks Institute and the Spanish Ministry of Education grant FPU2013-03792.Programa Oficial de Doctorado en Ingeniería MatemáticaPresidente: Alberto Tarable.- Secretario: José Antonio Cuesta Ruiz.- Vocal: Juan Julián Merelo Guervó
End-userApplication for Early Forest Fire Detection and Prevention
n this paper, we describe a Web application that has been designed and implemented by Fulda University of Applied Sciences in the context of the ASPires project. The application extends the functionality available to Crisis Management Centers (CMC). Actual readings from sensors installed in the test areas, for example national parks, are made available to CMC personnel, as well as pictures from cameras that are either mounted on stationary observation towers or taken by Unmanned Aerial Vehicles (UAVs) in the area of an actual of supposed forest fire. Data are transmitted to the Aspires cloud and delivered swiftly to the Web application via an open interface. Furthermore, fire alarms raised by novel detection algorithms are forwarded automatically to the application. This clearly improves the potential for the early detection of forest fires in rural areas