Blockchain-based secure and fair crowdsourcing scheme

The crowdsourcing schemes which utilize the social network to solve complex tasks are an important part of open cooperation over the Internet. Although blockchain-based crowdsourcing schemes have considerable advantages in decentralization and data sharing, there is still a challenge to gurantee the security of crowdsourced-sensitive information and the fairness of crowdsourcing on the blockchain. To this end, this article investigates a crowdsourcing scheme based on blockchain. First, we define the basic requirements of blockchain-based crowdsourcing schemes including fairness, confidentiality, and integrity. And then, using secure hash, commitment, and homomorphic encryption, we propose a blockchain-based secure and fair crowdsourcing scheme, that is, BFC. The analysis results show that our scheme can satisfy the above requirements. Finally, the experimental results show that the computational overhead of the BFC scheme is acceptable to both the requester and the workers. In a word, our proposed crowdsourcing scheme has good expansibility in reality.

[1]  Kwong-Sak Leung,et al.  A Survey of Crowdsourcing Systems , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[2]  Larry Carter,et al.  Universal Classes of Hash Functions , 1979, J. Comput. Syst. Sci..

[3]  Rüdiger Schollmeier,et al.  A definition of peer-to-peer networking for the classification of peer-to-peer architectures and applications , 2001, Proceedings First International Conference on Peer-to-Peer Computing.

[4]  Martin Wattenberg,et al.  A fuzzy commitment scheme , 1999, CCS '99.

[5]  Athanasios V. Vasilakos,et al.  TRAC: Truthful auction for location-aware collaborative sensing in mobile crowdsourcing , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[6]  Fernando González-Ladrón-de-Guevara,et al.  Towards an integrated crowdsourcing definition , 2012, J. Inf. Sci..

[7]  Sohrab Rahimi,et al.  Hidden style in the city: an analysis of geolocated airbnb rental images in ten major cities , 2016, UrbanGIS '16.

[8]  Yuan Lu,et al.  ZebraLancer: Private and Anonymous Crowdsourcing System atop Open Blockchain , 2018, 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS).

[9]  Daren C. Brabham Crowdsourcing as a Model for Problem Solving , 2008 .

[10]  Yang Gao,et al.  An incentive mechanism with privacy protection in mobile crowdsourcing systems , 2016, Comput. Networks.

[11]  Craig Gentry,et al.  A fully homomorphic encryption scheme , 2009 .

[12]  Yuval Peres,et al.  Approval Voting and Incentives in Crowdsourcing , 2015, ICML.

[13]  Ximeng Liu,et al.  An Efficient Privacy-Preserving Outsourced Calculation Toolkit With Multiple Keys , 2016, IEEE Transactions on Information Forensics and Security.

[14]  Chao Yang,et al.  LIP-PA: A Logistics Information Privacy Protection Scheme with Position and Attribute-Based Access Control on Mobile Devices , 2018, Wirel. Commun. Mob. Comput..

[15]  Chao Yang,et al.  BMPLS: Blockchain-Based Multi-level Privacy-Preserving Location Sharing Scheme for Telecare Medical Information Systems , 2018, Journal of Medical Systems.

[16]  Björn Hartmann,et al.  Collaboratively crowdsourcing workflows with turkomatic , 2012, CSCW.

[17]  Jianfeng Ma,et al.  Universally composable one-time signature and broadcast authentication , 2010, Science China Information Sciences.

[18]  Alon Y. Halevy,et al.  Crowdsourcing systems on the World-Wide Web , 2011, Commun. ACM.

[19]  Robert H. Deng,et al.  CrowdBC: A Blockchain-Based Decentralized Framework for Crowdsourcing , 2019, IEEE Transactions on Parallel and Distributed Systems.

[20]  Chao Yang,et al.  Universally composable secure positioning in the bounded retrieval model , 2015, Science China Information Sciences.

[21]  Anura P. Jayasumana,et al.  Collaborative applications over peer-to-peer systems–challenges and solutions , 2013, Peer Peer Netw. Appl..

[22]  Ivan Damgård,et al.  Semi-Homomorphic Encryption and Multiparty Computation , 2011, IACR Cryptol. ePrint Arch..

[23]  Robert H. Deng,et al.  Privacy-Preserving Outsourced Calculation on Floating Point Numbers , 2016, IEEE Transactions on Information Forensics and Security.

[24]  Ping Wang,et al.  Targeted Online Password Guessing: An Underestimated Threat , 2016, CCS.

[25]  Mihaela van der Schaar,et al.  Reputation-based incentive protocols in crowdsourcing applications , 2011, 2012 Proceedings IEEE INFOCOM.

[26]  Peng Liu,et al.  Using full duplex relaying in device-to-device (D2D) based wireless multicast services: a two-user case , 2014, Science China Information Sciences.

[27]  Melanie Swan,et al.  Blockchain: Blueprint for a New Economy , 2015 .

[28]  Jordi Herrera-Joancomartí,et al.  An Integrated Reward and Reputation Mechanism for MCS Preserving Users' Privacy , 2015, DPM/QASA@ESORICS.

[29]  Sherali Zeadally,et al.  Certificateless Public Key Authenticated Encryption With Keyword Search for Industrial Internet of Things , 2018, IEEE Transactions on Industrial Informatics.

[30]  Xuemin Shen,et al.  Security and privacy in mobile crowdsourcing networks: challenges and opportunities , 2015, IEEE Communications Magazine.

[31]  Thomas Shrimpton,et al.  Cryptographic Hash-Function Basics: Definitions, Implications, and Separations for Preimage Resistance, Second-Preimage Resistance, and Collision Resistance , 2004, FSE.

[32]  Jian Tang,et al.  Keep Your Promise: Mechanism Design Against Free-Riding and False-Reporting in Crowdsourcing , 2015, IEEE Internet of Things Journal.

[33]  Francesco Buccafurri,et al.  Tweetchain: An Alternative to Blockchain for Crowd-Based Applications , 2017, ICWE.