TY - GEN
T1 - Towards Secure and Trustworthy Crowdsourcing with Versatile Data Analytics
AU - Lian, Rui
AU - Zhou, Anxin
AU - Zheng, Yifeng
AU - Wang, Cong
N1 - Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).
PY - 2021
Y1 - 2021
N2 - Crowdsourcing enables the harnessing of crowd wisdom for data collection. While being widely successful, almost all existing crowdsourcing platforms store and process plaintext data only. Such a practice would allow anyone gaining access to the platform (e.g., attackers, administrators) to obtain the sensitive data, raising potential security and privacy concerns. If actively exploited, this not only infringes the data ownership of the crowdsourcing requester who solicits data, but also leaks the privacy of the workers who provide data. In this paper, we envision a crowdsourcing platform with built-in end-to-end encryption (E2EE), where the crowdsourced data remains always-encrypted secret to the platform. Such a design would serve as an in-depth defence strategy against data breach from both internal and external threats, and provide technical means for crowdsourcing service providers to meet various stringent regulatory compliance. We will discuss the technical requirements and related challenges to make this vision a reality, including: 1) assuring high-quality crowdsourced data to enhance data values, 2) enabling versatile data analytics to uncover data insights, 3) protecting data at the front-end to fully achieve E2EE, and 4) preventing the abuse of E2EE for practical deployment. We will briefly overview the limitations of prior arts in meeting all these requirements, and identify a few potential research directions for the roadmap ahead.
AB - Crowdsourcing enables the harnessing of crowd wisdom for data collection. While being widely successful, almost all existing crowdsourcing platforms store and process plaintext data only. Such a practice would allow anyone gaining access to the platform (e.g., attackers, administrators) to obtain the sensitive data, raising potential security and privacy concerns. If actively exploited, this not only infringes the data ownership of the crowdsourcing requester who solicits data, but also leaks the privacy of the workers who provide data. In this paper, we envision a crowdsourcing platform with built-in end-to-end encryption (E2EE), where the crowdsourced data remains always-encrypted secret to the platform. Such a design would serve as an in-depth defence strategy against data breach from both internal and external threats, and provide technical means for crowdsourcing service providers to meet various stringent regulatory compliance. We will discuss the technical requirements and related challenges to make this vision a reality, including: 1) assuring high-quality crowdsourced data to enhance data values, 2) enabling versatile data analytics to uncover data insights, 3) protecting data at the front-end to fully achieve E2EE, and 4) preventing the abuse of E2EE for practical deployment. We will briefly overview the limitations of prior arts in meeting all these requirements, and identify a few potential research directions for the roadmap ahead.
KW - Confidential computing
KW - Crowdsourcing
KW - Data protection
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U2 - 10.1007/978-3-030-91424-0_3
DO - 10.1007/978-3-030-91424-0_3
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 978-3-030-91423-3
T3 - Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
SP - 42
EP - 53
BT - Quality, Reliability, Security and Robustness in Heterogeneous Systems
A2 - Yuan, Xingliang
A2 - Bao, Wei
A2 - Yi, Xun
A2 - Tran, Nguyen Hoang
PB - Springer
CY - Cham
T2 - 17th European Alliance for Innovation (EAI) International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness (QShine 2021)
Y2 - 29 November 2021 through 30 November 2021
ER -