Privacy in Crowdsourcing: a Review of the Threats and Challenges

dc.contributor.authorXia, Huichuan
dc.contributor.authorMcKernan, Brian
dc.date.accessioned2020-06-13T20:53:42Z
dc.date.available2020-06-13T20:53:42Z
dc.date.issued2020
dc.date.issued2020
dc.description.abstractCrowdsourcing platforms such as Amazon Mechanical Turk (MTurk) are popular and widely used in both academic and non-academic realms, but privacy threats and challenges in crowdsourcing have not been extensively reviewed. To help push the field forward in important new directions, this paper first reviews the privacy threats in different types of crowdsourcing based on Solove’s taxonomy of privacy and Brabham’s typology of crowdsourcing. Then, the paper explores the privacy challenges associated with the characteristics of crowdsourcing task, platform, requesters, and crowd workers. These privacy challenges are discussed and categorized into both theoretical and practical challenges. Based on the review and discussion, this paper proposes a set of strategies to better understand and address many of the privacy threats and challenges in crowdsourcing. Finally, the paper concludes by suggesting research implications for the future work.de
dc.identifier.doi10.1007/s10606-020-09374-0
dc.identifier.pissn1573-7551
dc.identifier.urihttps://doi.org/10.1007/s10606-020-09374-0
dc.identifier.urihttps://dl.eusset.eu/handle/20.500.12015/4056
dc.publisherSpringer
dc.relation.ispartofComputer Supported Cooperative Work (CSCW): Vol. 29, No. 3
dc.relation.ispartofseriesComputer Supported Cooperative Work (CSCW)
dc.subjectCrowdsourcing
dc.subjectPrivacy threats
dc.subjectPrivacy challenges
dc.subjectPrivacy protection
dc.subjectAmazon Mechanical Turk (MTurk)
dc.titlePrivacy in Crowdsourcing: a Review of the Threats and Challengesde
dc.typeText/Journal Article
gi.citation.endPage301
gi.citation.startPage263
gi.citations.count14
gi.citations.elementHuichuan Xia (2023): Undue Influence or Exploitation — A Qualitative Inquiry into an Ethical Dilemma Around Payment in Crowd Work-Based Research in the U.S., In: Computer Supported Cooperative Work (CSCW) 3(33), doi:10.1007/s10606-023-09472-9
gi.citations.elementAsif Laghari, Hui He, Asiya Khan, Rashid Laghari, Shoulin Yin, Jiachi Wang (2022): Crowdsourcing platform for QoE evaluation for cloud multimedia services, In: Computer Science and Information Systems 3(19), doi:10.2298/csis220322038l
gi.citations.elementYuxiang Ye, Huichuan Xia, Chang Liu (2024): “Our Privacy Needs to be Protected Equally as Everybody's”—A Preliminary Study of Crowdsourced Delivery Riders' Privacy Concerns in China, In: Proceedings of the Association for Information Science and Technology 1(61), doi:10.1002/pra2.1216
gi.citations.elementHuichuan Xia (2022): What scholars and <scp>IRBs</scp> talk when they talk about the Belmont principles in crowd <scp>work‐based</scp> research, In: Journal of the Association for Information Science and Technology 1(74), doi:10.1002/asi.24724
gi.citations.elementDominik Antonowicz, Regina Lenart-Gansiniec, Łukasz Sułkowski (2024): Możliwości wykorzystania crowdsourcingu naukowego do operacjonalizacji problemu badawczego w badaniach nad szkolnictwem wyższym, In: Przegląd Socjologii Jakościowej 3(20), doi:10.18778/1733-8069.20.3.08
gi.citations.elementEllen Z. Zhang, Yunguo Guan, Rongxing Lu, Harry Zhang (2024): An Efficient Heap Tree-Based Range Query Scheme Under Local Differential Privacy, In: IEEE Internet of Things Journal 11(11), doi:10.1109/jiot.2024.3371828
gi.citations.elementXiaoqian Jiang, Haiyang Diao, Cangqi Zhou, Jing Zhang (2024): Timeliness-Selective Incentive Federated Crowdsourcing, In: 2024 IEEE International Conference on Web Services (ICWS), doi:10.1109/icws62655.2024.00083
gi.citations.elementMasoud Kamali, Mohammad Reza Malek, Sara Saeedi, Steve Liang (2021): A Blockchain-Based Spatial Crowdsourcing System for Spatial Information Collection Using a Reward Distribution, In: Sensors 15(21), doi:10.3390/s21155146
gi.citations.elementJean Zahn, José Viterbo, Cristiano Maciel, Flavia Bernardini (2024): A Framework for Implanting Citizen-Sourcing Platforms in Municipal Ombudsman Offices, In: Proceedings of the 25th Annual International Conference on Digital Government Research, doi:10.1145/3657054.3657118
gi.citations.elementSami Alkhatib, Ryan Kelly, Jenny Waycott, George Buchanan, Marthie Grobler, Shuo Wang (2021): “Who Wants to Know all this Stuff?!”: Understanding Older Adults’ Privacy Concerns in Aged Care Monitoring Devices, In: Interacting with Computers 5(33), doi:10.1093/iwc/iwab029
gi.citations.elementYunhui Li, Liang Chang, Long Li, Xuguang Bao, Tianlong Gu (2021): Key Research Issues and Related Technologies in Crowdsourcing Data Collection, In: Wireless Communications and Mobile Computing 1(2021), doi:10.1155/2021/8745897
gi.citations.elementEllen Z. Zhang, Yunguo Guan, Yantao Yu, Rongxing Lu, Harry Zhang (2024): An Efficient Range Sum Query Scheme Under Local Differential Privacy, In: ICC 2024 - IEEE International Conference on Communications, doi:10.1109/icc51166.2024.10622289
gi.citations.elementHuichuan Xia (2022): The original sin of crowd work for human subjects research, In: Journal of Information, Communication and Ethics in Society 3(20), doi:10.1108/jices-12-2021-0126
gi.citations.elementXuefeng Zhang, Enjun Xia, Chao Shen, Jiafu Su (2022): Factors Influencing Solvers’ Behaviors in Knowledge-Intensive Crowdsourcing: A Systematic Literature Review, In: Journal of Theoretical and Applied Electronic Commerce Research 4(17), doi:10.3390/jtaer17040066

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