Privacy in Crowdsourcing: a Review of the Threats and Challenges

Xia, Huichuan; McKernan, Brian

doi:10.1007/s10606-020-09374-0

Cited by 16 publications

(14 citation statements)

References 60 publications

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“…However, in crowdsourcing, malicious participants or the platform may deceive other stakeholders. Hence, the privacy threat has the unique characteristic of deceptive practices [88]. Further, developing effective strategies for protecting user privacy remains an open research problem in crowdsourcing.…”

Section: Discussionmentioning

confidence: 99%

Key Research Issues and Related Technologies in Crowdsourcing Data Collection

Chang

et al. 2021

Wireless Communications and Mobile Computing

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Crowdsourcing provides a distributed method to solve the tasks that are difficult to complete using computers and require the wisdom of human beings. Due to its fast and inexpensive nature, crowdsourcing is widely used to collect metadata and data annotation in many fields, such as information retrieval, machine learning, recommendation system, and natural language processing. Crowdsourcing helps enable the collection of rich and large-scale data, which promotes the development of researches driven by data. In recent years, a large amount of effort has been spent on crowdsourcing in data collection, to address the challenges, including quality control, cost control, efficiency, and privacy protection. In this paper, we introduce the concept and workflow of crowdsourcing data collection. Furthermore, we review the key research topics and related technologies in its workflow, including task design, task-worker matching, response aggregation, incentive mechanism, and privacy protection. Then, the limitations of the existing work are discussed, and the future development directions are identified.

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Section: Discussionmentioning

confidence: 99%

Key Research Issues and Related Technologies in Crowdsourcing Data Collection

Chang

et al. 2021

Wireless Communications and Mobile Computing

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“…Although using blockchain in crowdsourcing systems solves many problems related to security, transparency, traceability, and privacy [ 27 , 80 , 81 ], the current systems have many issues.…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, the lack of spatial factors to calculate the rewards of various users who participate in spatial crowdsourcing systems poses problems. Using users’ history in crowdsourcing systems reveals the users’ sensitive information and endangers their privacy [ 80 ]. The lack of secure and transparent distributed spatial crowdsourcing systems for collecting accurate information is a significant issue and less research has been carried out in this area [ 81 ].…”

Section: Related Workmentioning

confidence: 99%

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A Blockchain-Based Spatial Crowdsourcing System for Spatial Information Collection Using a Reward Distribution

Kamali

Malek

Saeedi

et al. 2021

Sensors

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Due to the increasing relevance of spatial information in different aspects of location-based services, various methods are used to collect this information. The use of crowdsourcing due to plurality and distribution is a remarkable strategy for collecting information, especially spatial information. Crowdsourcing can have a substantial effect on increasing the accuracy of data. However, many centralized crowdsourcing systems lack security and transparency due to a trusted party’s existence. With the emergence of blockchain technology, there has been an increase in security, transparency, and traceability in spatial crowdsourcing systems. In this paper, we propose a blockchain-based spatial crowdsourcing system in which workers confirm or reject the accuracy of tasks. Tasks are reports submitted by requesters to the system; a report comprises type and location. To our best knowledge, the proposed system is the first system that all participants receive rewards. This system considers spatial and non-spatial reward factors to encourage users’ participation in collecting accurate spatial information. Privacy preservation and security of spatial information are considered in the system. We also evaluated the system efficiency. According to the experiment results, using the proposed system, information accuracy increased by 40%, and the minimum time for reviewing reports by facilities reduced by 30%. Moreover, we compared the proposed system with the current centralized and distributed crowdsourcing systems. This comparison shows that, although our proposed system omits the user’s history to preserve privacy, it considers a consensus-based approach to guarantee submitted reports’ accuracy. The proposed system also has a reward mechanism to encourage more participation.

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“…One of the most costly bottlenecks of learning to detect user disengagement is to annotate many turn-level user engagement labels (Ghazarian et al, 2020). In addition, the data annotation process becomes more expensive and challenging for privacy-sensitive dialog corpora, due to the privacy concerns in crowdsourcing (Xia and McKernan, 2020).…”

Section: Introductionmentioning

confidence: 99%

HERALD: An Annotation Efficient Method to Detect User Disengagement in Social Conversations

Liang¹,

Liang²,

Yu³

2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Open-domain dialog systems have a usercentric goal: to provide humans with an engaging conversation experience. User engagement is one of the most important metrics for evaluating open-domain dialog systems, and could also be used as real-time feedback to benefit dialog policy learning. Existing work on detecting user disengagement typically requires hand-labeling many dialog samples. We propose HERALD, an efficient annotation framework that reframes the training data annotation process as a denoising problem. Specifically, instead of manually labeling training samples, we first use a set of labeling heuristics to label training samples automatically. We then denoise the weakly labeled data using the Shapley algorithm. Finally, we use the denoised data to train a user engagement detector. Our experiments show that HERALD improves annotation efficiency significantly and achieves 86% user disengagement detection accuracy in two dialog corpora.

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Privacy in Crowdsourcing: a Review of the Threats and Challenges

Cited by 16 publications

References 60 publications

Key Research Issues and Related Technologies in Crowdsourcing Data Collection

Key Research Issues and Related Technologies in Crowdsourcing Data Collection

A Blockchain-Based Spatial Crowdsourcing System for Spatial Information Collection Using a Reward Distribution

HERALD: An Annotation Efficient Method to Detect User Disengagement in Social Conversations

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