AntiCheetah: Trustworthy computing in an outsourced (cheating) environment

Pietro, Roberto Di; Lombardi, Flavio; Martinelli, Fabio; Sgandurra, Daniele

doi:10.1016/j.future.2015.02.004

Cited by 7 publications

(4 citation statements)

References 40 publications

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“…Re-execution can be implemented in several ways. In [10], the authors propose outsourcing computation to multiple Contractors in a multi-round approach. The Outsourcer sends different inputs to each Contractor in every round.…”

Section: Related Workmentioning

confidence: 99%

Verifying Outsourced Computation in an Edge Computing Marketplace

Harth-Kitzerow¹,

Garrido²

2022

Machine Learning &Amp; Applications

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An edge computing marketplace could enable IoT devices (Outsourcers) to outsource computation to any participating node (Contractors) in their proximity. In return, these nodes receive a reward for providing computation resources. In this work, we propose a scheme that verifies the integrity of arbitrary deterministic functions in the presence of both dishonest Outsourcers and Contractors who try to maximize their expected payoff. We compile a comprehensive set of threats for this adversary model and show that not all of these threats are addressed when combining verification techniques of related work. Our verification scheme fills the gap by detecting or preventing each identified threat. We tested our verification scheme with state-of-the-art pre-trained Convolutional Neural Network models designed for object detection. On all devices, our verification scheme causes less than 1ms computational overhead and a negligible network bandwidth overhead of at most 84 bytes per frame. Our implementation can also perform our verification scheme’s tasks parallel to the object detection to eliminate any latency overhead.

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Section: Related Workmentioning

confidence: 99%

Verifying Outsourced Computation in an Edge Computing Marketplace

Harth-Kitzerow¹,

Garrido²

2022

Machine Learning &Amp; Applications

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“…Recently, trust was introduced in the concept of social cloud [62,63,64,65,66,67,68,69,70]. In [63], Mohaisen et al employ trust as a metric to identify good workers for an outsourcer through her social network.…”

Section: Trust In Cloud Computingmentioning

confidence: 99%

“…In [68], Moyano et al proposed a framework to employ trust and reputation for cloud provider selection. In [70], Pietro et al proposed a multi-round approach, called AntiCheetah, to dynamically assign tasks to cloud nodes, accounting for their trustworthiness. In [64], Zhou et al studied the trust factors affecting potential partnerships in bike sharing systems.…”

Section: Trust In Cloud Computingmentioning

confidence: 99%

NeuralWalk: Trust Assessment in Online Social Networks with Neural Networks

Liu¹,

Li²,

Yang

2019

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications

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Assessing trust in online social networks (OSNs) is critical for many applications such as online marketing and network security. It is a challenging problem, however, due to the difficulties of handling complex social network topologies and conducting accurate assessment in these topologies. To address these challenges, we model trust by proposing the three-valued subjective logic (3VSL) model. 3VSL properly models the uncertainties that exist in trust, thus is able to compute trust in arbitrary graphs. We theoretically prove the capability of 3VSL based on the Dirichlet-Categorical (DC) distribution and its correctness in arbitrary OSN topologies. Based on the 3VSL model, we further design the AssessTrust (AT) algorithm to accurately compute the trust between any two users connected in an OSN. We validate 3VSL against two real-world OSN datasets: Advogato and Pretty Good Privacy (PGP). Experimental results indicate that 3VSL can accurately model the trust between any pair of indirectly connected users in the Advogato and PGP.

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“…Along with the rapid development of the Internet and online services, trust has been used in many applications for either improving users' quality of experience (QoE) or preventing the disturbance of malicious users. Recently, trust was introduced in the concept of social cloud [20,21,22,23,24,25,26,27,28]. Trust is also introduced in cyber-physical and edge computing systems, e.g., wireless sensor networks [29,30,31,32,33,34,35,36,37,38] and vehicular networks [39,40,41,42].…”

Section: Related Workmentioning

confidence: 99%

A Probabilistic Graph Model for Trust Opinion Estimation in Online Social Networks

Liu,

Yang

2019

Preprint

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Trust assessment plays a key role in many online applications, such as online money lending, product reviewing and active friending. Trust models usually employ a group of parameters to represent the trust relation between a trustor-trustee pair. These parameters are originated from the trustors bias and opinion on the trustee. Naturally, these parameters can be regarded as a vector. To address this problem, we propose a framework to accurately convert the single values to the parameters needed by 3VSL. The framework firstly employs a probabilistic graph model (PGM) to derive the trustors opinion and bias to his rating on the trustee.

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AntiCheetah: Trustworthy computing in an outsourced (cheating) environment

Cited by 7 publications

References 40 publications

Verifying Outsourced Computation in an Edge Computing Marketplace

Verifying Outsourced Computation in an Edge Computing Marketplace

NeuralWalk: Trust Assessment in Online Social Networks with Neural Networks

A Probabilistic Graph Model for Trust Opinion Estimation in Online Social Networks

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