1,354 research outputs found
Achieving reliability and fairness in online task computing environments
MenciĆ³n Internacional en el tĆtulo 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 workersā behavior through two realistic models:
(1) the āerror probability modelā 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 ārationality modelā 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 workersā 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) āvotingā and (2)
āauditingā 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 workersā 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 masterās 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 workerās 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 IngenierĆa MatemĆ”ticaPresidente: Alberto Tarable.- Secretario: JosĆ© Antonio Cuesta Ruiz.- Vocal: Juan JuliĆ”n Merelo GuervĆ³
Mitigating Colluding Attacks in Online Social Networks and Crowdsourcing Platforms
Online Social Networks (OSNs) have created new ways for people to communicate, and for companies to engage their customers -- with these new avenues for communication come new vulnerabilities that can be exploited by attackers. This dissertation aims to investigate two attack models: Identity Clone Attacks (ICA) and Reconnaissance Attacks (RA). During an ICA, attackers impersonate users in a network and attempt to infiltrate social circles and extract confidential information. In an RA, attackers gather information on a target\u27s resources, employees, and relationships with other entities over public venues such as OSNs and company websites. This was made easier for the RA to be efficient because well-known social networks, such as Facebook, have a policy to force people to use their real identities for their accounts. The goal of our research is to provide mechanisms to defend against colluding attackers in the presence of ICA and RA collusion attacks. In this work, we consider a scenario not addressed by previous works, wherein multiple attackers collude against the network, and propose defense mechanisms for such an attack. We take into account the asymmetric nature of social networks and include the case where colluders could add or modify some attributes of their clones. We also consider the case where attackers send few friend requests to uncover their targets.
To detect fake reviews and uncovering colluders in crowdsourcing, we propose a semantic similarity measurement between reviews and a community detection algorithm to overcome the non-adversarial attack. ICA in a colluding attack may become stronger and more sophisticated than in a single attack. We introduce a token-based comparison and a friend list structure-matching approach, resulting in stronger identifiers even in the presence of attackers who could add or modify some attributes on the clone. We also propose a stronger RA collusion mechanism in which colluders build their own legitimacy by considering asymmetric relationships among users and, while having partial information of the networks, avoid recreating social circles around their targets. Finally, we propose a defense mechanism against colluding RA which uses the weakest person (e.g., the potential victim willing to accept friend requests) to reach their target
Trust-based algorithms for fusing crowdsourced estimates of continuous quantities
Crowdsourcing has provided a viable way of gathering information at unprecedented volumes and speed by engaging individuals to perform simple microātasks. In particular, the crowdsourcing paradigm has been successfully applied to participatory sensing, in which the users perform sensing tasks and provide data using their mobile devices. In this way, people can help solve complex environmental sensing tasks, such as weather monitoring, nuclear radiation monitoring and cell tower mapping, in a highly decentralised and parallelised fashion. Traditionally, crowdsourcing technologies were primarily used for gathering data for classifications and image labelling tasks. In contrast, such crowdābased participatory sensing poses new challenges that relate to (i) dealing with humanāreported sensor data that are available in the form of continuous estimates of an observed quantity such as a location, a temperature or a sound reading, (ii) dealing with possible spatial and temporal correlations within the data and (ii) issues of data trustworthiness due to the unknown capabilities and incentives of the participants and their devices. Solutions to these challenges need to be able to combine the data provided by multiple users to ensure the accuracy and the validity of the aggregated results. With this in mind, our goal is to provide methods to better aid the aggregation process of crowdāreported sensor estimates of continuous quantities when data are provided by individuals of varying trustworthiness. To achieve this, we develop a trustābased in- formation fusion framework that incorporates latent trustworthiness traits of the users within the data fusion process. Through this framework, we develop a set of four novel algorithms (MaxTrust, BACE, TrustGP and TrustLGCP) to compute reliable aggregations of the usersā reports in both the settings of observing a stationary quantity (Max- Trust and BACE) and a spatially distributed phenomenon (TrustGP and TrustLGCP). The key feature of all these algorithm is the ability of (i) learning the trustworthiness of each individual who provide the data and (ii) exploit this latent userās trustworthiness information to compute a more accurate fused estimate. In particular, this is achieved by using a probabilistic framework that allows our methods to simultaneously learn the fused estimate and the usersā trustworthiness from the crowd reports. We validate our algorithms in four key application areas (cell tower mapping, WiFi networks mapping, nuclear radiation monitoring and disaster response) that demonstrate the practical impact of our framework to achieve substantially more accurate and informative predictions compared to the existing fusion methods. We expect that results of this thesis will allow to build more reliable data fusion algorithms for the broad class of humanācentred information systems (e.g., recommendation systems, peer reviewing systems, student grading tools) that are based on making decisions upon subjective opinions provided by their users
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