Building a Secure Knowledge Marketplace Over Crowdsensed Data Streams

被引:19
作者
Cai, Chengjun [1 ,2 ]
Zheng, Yifeng [3 ]
Zhou, Anxin [1 ,2 ]
Wang, Cong [1 ,2 ]
机构
[1] City Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China
[2] City Univ Hong Kong, Shenzhen Res Inst, Shenzhen 518057, Peoples R China
[3] CSIRO, Data61, Marsfield, NSW 2122, Australia
基金
中国国家自然科学基金;
关键词
Cryptography; Servers; Reliability; Monitoring; Additives; Sensors; Encrypted blockchain applications; truth discovery; privacy; crowdsensing systems;
D O I
10.1109/TDSC.2019.2958901
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
It is increasingly popular to leverage the wisdom of crowd for knowledge discovery and monetization. Among others, crowdsensing with truth discovery has emerged as a promising way for leveraging the crowd wisdom, which can mine reliable knowledge from the generally unreliable sensory data contributed collected from diverse sources. Building a knowledge marketplace based on crowdsensing with truth discovery for knowledge discovery and monetization, however, is non-trivial and has to overcome several challenges. First, the sensory data should be protected as they may carry sensitive information. Second, many real crowdsensing applications usually yield sensory data in a streaming fashion, posing the demand that truth discovery should be conducted over data streams to continuously mine reliable knowledge in each data collection epoch. Third, knowledge monetization should be well treated, fully addressing the practical needs of parties in the monetization ecosystem. In this article, we take the first research attempt and propose a new full-fledged framework for building a secure knowledge marketplace over crowdsensed data streams. Our marketplace supports secure monetization of reliable knowledge mined privately from data streams in crowdsensing applications. Our framework leverages lightweight cryptographic techniques like additive secret sharing to enable privacy-preserving streaming truth discovery, continuously producing reliable knowledge over data streams. For monetization of the learned truth, i.e., knowledge, we resort to the emerging blockchain technology and deliver a tailored and full-fledged design, which promises monetization fairness, knowledge confidentiality, and streamlined processing. Extensive experiments on Amazon cloud and Ethereum blockchain demonstrate the practically affordable performance of our design.
引用
收藏
页码:2601 / 2616
页数:16
相关论文
共 68 条
  • [1] Hyperledger Fabric: A Distributed Operating System for Permissioned Blockchains
    Androulaki, Elli
    Barger, Artem
    Bortnikov, Vita
    Cachin, Christian
    Christidis, Konstantinos
    De Caro, Angelo
    Enyeart, David
    Ferris, Christopher
    Laventman, Gennady
    Manevich, Yacov
    Muralidharan, Srinivasan
    Murthy, Chet
    Binh Nguyen
    Sethi, Manish
    Singh, Gari
    Smith, Keith
    Sorniotti, Alessandro
    Stathakopoulou, Chrysoula
    Vukolic, Marko
    Cocco, Sharon Weed
    Yellick, Jason
    [J]. EUROSYS '18: PROCEEDINGS OF THE THIRTEENTH EUROSYS CONFERENCE, 2018,
  • [2] [Anonymous], 2018, MIST BROWSER BETA
  • [3] [Anonymous], 2018, SMART DUBLIN NOISE M
  • [4] [Anonymous], 2018, ARXIV180602057
  • [5] [Anonymous], 2018, ETHEREUM LIGHT CLIEN
  • [6] [Anonymous], 1981, ADV CRYPT IEEE WORKS
  • [7] BEAVER D, 1992, LECT NOTES COMPUT SC, V576, P420
  • [8] Bilogrevic I, 2014, LECT NOTES COMPUT SC, V8713, P128, DOI 10.1007/978-3-319-11212-1_8
  • [9] SoK: Research Perspectives and Challenges for Bitcoin and Cryptocurrencies
    Bonneau, Joseph
    Miller, Andrew
    Clark, Jeremy
    Narayanan, Arvind
    Kroll, Joshua A.
    Felten, Edward W.
    [J]. 2015 IEEE SYMPOSIUM ON SECURITY AND PRIVACY SP 2015, 2015, : 104 - 121
  • [10] Towards Private, Robust, and Verifiable Crowdsensing Systems via Public Blockchains
    Cai, Chengjun
    Zheng, Yifeng
    Du, Yuefeng
    Qin, Zhan
    Wang, Cong
    [J]. IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING, 2021, 18 (04) : 1893 - 1907