From Task Tuning to Task Assignment in Privacy-Preserving Crowdsourcing Platforms

Duguépéroux, Joris; Allard, Tristan

doi:10.1007/978-3-662-62271-1_3

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…Given a multi-agent setting, this can be done in a distributed way by using a selection algorithm that finds the k th smallest element(s) as long as the data of the agents can be shared with other agents in their coalition. If data cannot be shared with other members, calculating the median in a privacy-preserving way demands a more complex scheme [25] and is not trivial. The theoretical breakdown point of the median characterises it as one of the most robust estimators and is for the worst case given with 1 2 .…”

Section: ) Medianmentioning

confidence: 99%

On the Design of Decentralised Data Markets

Kharman¹,

Jursitzky²,

Zhao³

et al. 2022

Preprint

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We present an architecture to implement a decentralised data market, whereby agents are incentivised to collaborate to crowd-source their data. The architecture is designed to reward data that furthers the market's collective goal, and distributes reward fairly to all those that contribute with their data. This is achieved leveraging the concept of Shapley's value from Game Theory. Furthermore, we introduce trust assumptions based on provable honesty, as opposed to wealth, or computational power, and we aim to reward agents that actively enable the functioning of the market. In order to evaluate the resilience of the architecture, we characterise its breakdown points for various adversarial threat models and we validate our analysis through extensive Monte Carlo simulations.

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Section: ) Medianmentioning

confidence: 99%

On the Design of Decentralised Data Markets

Kharman¹,

Jursitzky²,

Zhao³

et al. 2022

Preprint

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show abstract

“…Moreover, the preference information of workers (that is, the set of tasks that workers want to do) will also reveal the location information of workers from the side. The reason is that workers may first choose the tasks as the candidate task whose locations are close to their homes or work unit [6]- [8].…”

Section: Introductionmentioning

confidence: 99%

Differentially Private Task Allocation Algorithm Under Preference Protection

Han

Cai

2022

IEEE Access

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Mobile crowdsensing has been widely applied as a kind of perception paradigm, and task allocation is a fundamental research issue in mobile crowdsensing. Existing task allocation algorithms under differential privacy are not suitable for preference protection scenarios as they may inject too much noise. To this end, in this paper, we propose a differentially private task allocation algorithm with preference protection, referred to as SLEPT. In SLEPT, we divide privacy budget into three parts. Specifically, we first use one part of privacy budget to perturb the location of each worker. Then we use another part of privacy budget to perturb the preference information of him. In particular, to relieve the problem that perturbation may lead to that tasks will not be allocated, we propose a two-phase preference collection mechanism called TPC. Finally, we propose a task allocation sequential updating mechanism TASU using the remaining privacy budget. It aims to reduce the travel distance of workers and improve the success rate of task allocation. Theoretical analysis shows that SLEPT satisfies differential privacy. Time complexity analysis shows that it is linearly related to the number of tasks. The results on two public datasets verify the effectiveness of SLEPT. It is worth noting that although SLEPT is proposed for task allocation, its idea is also applicable to other crowdsensing scenarios, such as high-dimensional data collection.

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An adversarially robust data-market for spatial, crowd-sourced data

Kharman,

Jursitzky,

Zhou

et al. 2024

Distrib. Ledger Technol.

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We describe an architecture for a decentralised data market for applications in which agents are incentivised to collaborate to crowd-source their data. The architecture is designed to reward data that furthers the market's collective goal, and distributes reward fairly to all those that contribute with their data. We show that the architecture is resilient to Sybil , wormhole , and data poisoning attacks. In order to evaluate the resilience of the architecture, we characterise its breakdown points for various adversarial threat models in an automotive use case.

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From Task Tuning to Task Assignment in Privacy-Preserving Crowdsourcing Platforms

Cited by 3 publications

References 30 publications

On the Design of Decentralised Data Markets

On the Design of Decentralised Data Markets

Differentially Private Task Allocation Algorithm Under Preference Protection

An adversarially robust data-market for spatial, crowd-sourced data

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