群智感知应用中基于区块链的激励机制
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摘要
群智感知应用利用无处不在的移动用户的智能终端采集大规模感知数据,感知任务的高效执行依赖于高技能用户的参与,这些用户应被给予相应的报酬来弥补其在执行感知任务中的资源消耗.现有的激励机制难以满足群智感知分布式环境下安全性需求.如信誉机制易遭受sybil攻击和洗白攻击,这让诚实用户受到损失.互惠机制不够灵活.而基于货币的激励机制能弥补信誉和互惠机制的缺点,但是这种机制要么依赖中央机构,要么无法给出一个安全可信的数字货币中心.提出了一种群智感知应用中基于区块链的激励机制,该机制采用区块链安全的分布式架构,平台和感知用户作为区块链中的节点进行感知任务执行,其交易关系被记录在区块链中,由区块链中的矿工进行验证,有效防止感知平台发起的共谋攻击,克服了可信第三方面临的安全隐患.通过仿真实验,验证了基于区块链的机制的有效性和可行性.
Crowdsensing applications collect large-scale sensing data by ubiquitous users carrying with smart devices. In crowdsensing applications, the quality of sensing data depends on the participation of high-skilled users, thus the users should be compensated for their resource consumption in the sensing task. Existing incentive mechanisms are difficult to meet the security requirements in the distributed environment of crowdsensing applications. For example, the reputation mechanism may suffer sybil attacks and whitewash attacks, which is unfair to honest users. The reciprocity mechanism is not flexible. The monetary scheme could make up the defects of the two preceding mechanisms, but it either relys on a central authority or does not give an explicit digital currency system which is provably secure, leading to possible system collapses or potential privacy disclosure caused by the ‘trusted' center. In this paper, we propose a blockchain based incentive mechanism which uses a distributed architecture that is proved to be secure. In this distributed secure architecture, the participant users can be regarded as the nodes in a blockchain, and the payment transactions are recorded in the blockchain. The transactions will be verified by a majority of miners in the blockchain and they cannot be modified after being accepted by the miners. The incentive mechanism can prevent a part of participant users launching collusion attacks, and avoid the security threats brought by a trusted third party. Simulation experiments demonstrate the security strength and feasibility of the proposed incentive mechanism.
Crowdsensing applications collect large-scale sensing data by ubiquitous users carrying with smart devices. In crowdsensing applications, the quality of sensing data depends on the participation of high-skilled users, thus the users should be compensated for their resource consumption in the sensing task. Existing incentive mechanisms are difficult to meet the security requirements in the distributed environment of crowdsensing applications. For example, the reputation mechanism may suffer sybil attacks and whitewash attacks, which is unfair to honest users. The reciprocity mechanism is not flexible. The monetary scheme could make up the defects of the two preceding mechanisms, but it either relys on a central authority or does not give an explicit digital currency system which is provably secure, leading to possible system collapses or potential privacy disclosure caused by the ‘trusted' center. In this paper, we propose a blockchain based incentive mechanism which uses a distributed architecture that is proved to be secure. In this distributed secure architecture, the participant users can be regarded as the nodes in a blockchain, and the payment transactions are recorded in the blockchain. The transactions will be verified by a majority of miners in the blockchain and they cannot be modified after being accepted by the miners. The incentive mechanism can prevent a part of participant users launching collusion attacks, and avoid the security threats brought by a trusted third party. Simulation experiments demonstrate the security strength and feasibility of the proposed incentive mechanism.
引文
[1]Xiong Jinbo, Ma Rong, Niu Ben, et al. Privacy protection Incentive mechanism based on user-union matching in mobile crowdsensing[J]. Journal of Computer Research and Development, 2018, 55(7): 1359- 1370 (in Chinese)(金波, 马蓉, 牛犇, 等. 移动群智感知中基于用户联盟匹配的隐私保护激励机制[J]. 计算机研究与发展, 2018, 55(7): 1359- 1370)
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[6]Xie Hong, Lui J C S, Towsley D. Incentive and reputation mechanisms for online crowdsourcing systems[C] //Proc of the 23rd Int Symp on Quality of Service (IWQoS). Piscataway, NJ: IEEE, 2015: 207- 212
[7]Mohannad A Alswailim, Hossam S Hassanein, Zulkernine M. A reputation system to evaluate participants for participatory sensing[C] //Proc of the 59th Int Symp on Global Communications Conf(Globcom). Piscataway, NJ: IEEE, 2016: 16655361
[8]Peng Dan, Wu Fan, Chen Guihai. Pay as how well you do: A quality based incentive mechanism for crowdsensing[C] //Proc of the 16th ACM Int Symp on Mobile Ad Hoc Net-working and Computing. New York: ACM, 2015: 177- 186
[9]Gong Xiaowen, Chen Xu, Zhang Junshan, et al. Exploiting social trust assisted reciprocity (STAR) toward utility-optimal socially-aware crowdsensing[J]. IEEE Transactions on Signal and Information Processing over Networks, 2015, 1(3): 195- 208
[10]Zhang Yuchen, Chen Xi, Zhou Dengyong, et al. Spectral methods meet EM: A provably optimal algorithm for crowdsourcing[C] //Advances in Neural Information Processing Systems 27(NIPS 2014). Cambridge, MA: MIT Press, 2014: 1260- 1268
[11]Wang Jing, Ipeirotis P G, Provost F. Quality-based pricing for crowdsourced workers[J/OL]. Social Science Research Network, 2013 [2017-12-07]. http://www.ipeirotis.com/wp-content/uploads/2013/06/CBA-13-06.pdf
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[13]Bentov I, Kumaresan R. How to use bitcoin to design fair protocols[C] //Proc of the 34th Int Cryptology Conf. Berlin: Springer, 2014: 421- 439
[14]Andrychowicz M, Dziembowski S, Malinowski D, et al. Secure multiparty computations on bitcoin[C] //Proc of the 35th Symp on Security and Privacy (SP). Piscataway, NJ: IEEE, 2014: 443- 458
[15]Andrychowicz M, Dziembowski S, Malinowski D, et al. Fair two-party computations via bitcoin deposits[C] //Proc of the Int Conf on Financial Cryptography and Data Security. Berlin: Springer, 2014: 105- 121
[16]Kumaresan R, Bentov I. How to use bitcoin to incentivize correct computations[C] //Proc of the 21st ACM SIGSAC Conf on Computer and Communications Security. New York: ACM, 2014: 30- 41
[17]Li Ming, Weng Jian, Yang Anjia, et al. CrowdBC: A blockchain-based decentralized framework for crowdsourcing[OL]. [2017-09-07]. https://allquantor.at/blockchain bib/pdf/li2017crowdbc.pdf
[18]Nakamoto S. Bitcoin: A peer-to-peer electronic cash system[OL]. 2008 [2017-09-01]. https://bitcoin.org/bitcoin.pdf
[19]Antonopoulos A M. Mastering Bitcoin: Unlocking Digital Cryptocurrencies[M]. Sebastopol, CA: O’Reilly Media, Inc, 2014
[20]Kogias E K, Jovanovic P, Gailly N, et al. Enhancing bitcoin security and performance with strong consistency via collective signing[C] //Proc of the 25th USENIX Security Symp (USENIX Security 16). Berkeley, CA: USENIX Association, 2016: 279- 296
[21]Ambikairajah E, Davis A G, Wong W T K. Auditory masking and MPEG-1 audio compression[J]. Electronics & Communication Engineering Journal, 1997, 9(4): 165- 175
[22]Theo D, Viviane J, Wolfgang M, et al. Noisetube[OL]. 2008 [2017-09-07]. http://www.noisetube.net/
[23]Dagum P, Luby M. Approximating probabilistic inference in Bayesian belief networks is NP-hard[J]. Artificial Intelligence, 1993, 60(1): 141- 153
[24]Kumaresan R, Moran T, Bentov I. How to use bitcoin to play decentralized poker[C] //Proc of the 22nd ACM SIGSAC Conf on Computer and Communications Security. New York: ACM, 2015: 195- 206
[25]Dawid A P, Skene A M. Maximum likelihood estimation of observer error-rates using the EM algorithm[J]. Journal of the Royal Statistical Society, 1979, 28(1): 20- 28
[26]Luu L, Teutsch J, Kulkarni R, et al. Demystifying incentives in the consensus computer[C] //Proc of the 22nd ACM SIGSAC Conf on Computer and Communications Security. New York: ACM, 2015: 706- 719
[27]Wang Ye. A new watermarking method of digital audio content for copyright protection[C] //Proc of the 4th Int Conf on Signal Processing (ICSP’98). Piscataway, NJ: IEEE, 1998: 1420- 1423
[2]Zhang Yu, van der Schaar M. Reputation-based incentive protocols in crowdsourcing applications[C] //Proc of the 31st Int Conf on Computer Communications (IEEE INFOCOM 2012). Piscataway, NJ: IEEE, 2012: 2140- 2148
[3]Douceur J R. The sybil attack[C] //Proc of the 1st Int Workshop on Peer-to-Peer Systems. Berlin: Springer, 2002: 251- 260
[4]Feldman M, Papadimitriou C, Chuang J, et al. Free-riding and whitewashing in peer-to-peer systems[J]. IEEE Journal on Selected Areas in Communications, 2006, 24(5): 1010- 1019
[5]Gong Xiaowen, Chen Xu, Zhang Junshan, et al. From social trust assisted reciprocity (STAR) to utility-optimal mobile crowdsensing[C] //Proc of the 2nd IEEE Global Conf on Signal and Information Processing (GlobalSIP). Piscataway, NJ: IEEE, 2014: 742- 745
[6]Xie Hong, Lui J C S, Towsley D. Incentive and reputation mechanisms for online crowdsourcing systems[C] //Proc of the 23rd Int Symp on Quality of Service (IWQoS). Piscataway, NJ: IEEE, 2015: 207- 212
[7]Mohannad A Alswailim, Hossam S Hassanein, Zulkernine M. A reputation system to evaluate participants for participatory sensing[C] //Proc of the 59th Int Symp on Global Communications Conf(Globcom). Piscataway, NJ: IEEE, 2016: 16655361
[8]Peng Dan, Wu Fan, Chen Guihai. Pay as how well you do: A quality based incentive mechanism for crowdsensing[C] //Proc of the 16th ACM Int Symp on Mobile Ad Hoc Net-working and Computing. New York: ACM, 2015: 177- 186
[9]Gong Xiaowen, Chen Xu, Zhang Junshan, et al. Exploiting social trust assisted reciprocity (STAR) toward utility-optimal socially-aware crowdsensing[J]. IEEE Transactions on Signal and Information Processing over Networks, 2015, 1(3): 195- 208
[10]Zhang Yuchen, Chen Xi, Zhou Dengyong, et al. Spectral methods meet EM: A provably optimal algorithm for crowdsourcing[C] //Advances in Neural Information Processing Systems 27(NIPS 2014). Cambridge, MA: MIT Press, 2014: 1260- 1268
[11]Wang Jing, Ipeirotis P G, Provost F. Quality-based pricing for crowdsourced workers[J/OL]. Social Science Research Network, 2013 [2017-12-07]. http://www.ipeirotis.com/wp-content/uploads/2013/06/CBA-13-06.pdf
[12]Gervais A, Karame G O, Wüst K, et al. On the security and performance of proof of work blockchains[C] //Proc of the 23rd SIGSAC Conf on Computer and Communications Security. New York: ACM, 2016: 3- 16
[13]Bentov I, Kumaresan R. How to use bitcoin to design fair protocols[C] //Proc of the 34th Int Cryptology Conf. Berlin: Springer, 2014: 421- 439
[14]Andrychowicz M, Dziembowski S, Malinowski D, et al. Secure multiparty computations on bitcoin[C] //Proc of the 35th Symp on Security and Privacy (SP). Piscataway, NJ: IEEE, 2014: 443- 458
[15]Andrychowicz M, Dziembowski S, Malinowski D, et al. Fair two-party computations via bitcoin deposits[C] //Proc of the Int Conf on Financial Cryptography and Data Security. Berlin: Springer, 2014: 105- 121
[16]Kumaresan R, Bentov I. How to use bitcoin to incentivize correct computations[C] //Proc of the 21st ACM SIGSAC Conf on Computer and Communications Security. New York: ACM, 2014: 30- 41
[17]Li Ming, Weng Jian, Yang Anjia, et al. CrowdBC: A blockchain-based decentralized framework for crowdsourcing[OL]. [2017-09-07]. https://allquantor.at/blockchain bib/pdf/li2017crowdbc.pdf
[18]Nakamoto S. Bitcoin: A peer-to-peer electronic cash system[OL]. 2008 [2017-09-01]. https://bitcoin.org/bitcoin.pdf
[19]Antonopoulos A M. Mastering Bitcoin: Unlocking Digital Cryptocurrencies[M]. Sebastopol, CA: O’Reilly Media, Inc, 2014
[20]Kogias E K, Jovanovic P, Gailly N, et al. Enhancing bitcoin security and performance with strong consistency via collective signing[C] //Proc of the 25th USENIX Security Symp (USENIX Security 16). Berkeley, CA: USENIX Association, 2016: 279- 296
[21]Ambikairajah E, Davis A G, Wong W T K. Auditory masking and MPEG-1 audio compression[J]. Electronics & Communication Engineering Journal, 1997, 9(4): 165- 175
[22]Theo D, Viviane J, Wolfgang M, et al. Noisetube[OL]. 2008 [2017-09-07]. http://www.noisetube.net/
[23]Dagum P, Luby M. Approximating probabilistic inference in Bayesian belief networks is NP-hard[J]. Artificial Intelligence, 1993, 60(1): 141- 153
[24]Kumaresan R, Moran T, Bentov I. How to use bitcoin to play decentralized poker[C] //Proc of the 22nd ACM SIGSAC Conf on Computer and Communications Security. New York: ACM, 2015: 195- 206
[25]Dawid A P, Skene A M. Maximum likelihood estimation of observer error-rates using the EM algorithm[J]. Journal of the Royal Statistical Society, 1979, 28(1): 20- 28
[26]Luu L, Teutsch J, Kulkarni R, et al. Demystifying incentives in the consensus computer[C] //Proc of the 22nd ACM SIGSAC Conf on Computer and Communications Security. New York: ACM, 2015: 706- 719
[27]Wang Ye. A new watermarking method of digital audio content for copyright protection[C] //Proc of the 4th Int Conf on Signal Processing (ICSP’98). Piscataway, NJ: IEEE, 1998: 1420- 1423
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