Journal of Computer Science and Technology ›› 2022, Vol. 37 ›› Issue (1): 266-276.doi: 10.1007/s11390-021-0583-x

Special Issue: Computer Networks and Distributed Computing

• Regular Paper • Previous Articles    

A Blockchain-Based Protocol for Malicious Price Discrimination

Li-De Xue1 (薛立德), Ya-Jun Liu2 (刘亚军), Wei Yang1,* (杨威), Member, IEEE, Wei-Lin Chen1 (陈蔚林), and Liu-Sheng Huang1 (黄刘生), Member, IEEE        

  1. 1School of Computer Science and Technology, University of Science and Technology of China, Hefei 230000, China
    2Information Department, the First Affiliated Hospital of Soochow University, Suzhou 215000, China
  • Received:2020-05-06 Revised:2021-12-01 Accepted:2021-12-29 Online:2022-01-28 Published:2022-01-28
  • Contact: Wei Yang E-mail:qubit@ustc.edu.cn
  • About author:Wei Yang is an associate professor in School of Computer Science and Technology at the University of Science and Technology of China, Hefei. In 2007, he received his Ph.D. degree in computer science from University of Science and Technology of China, Hefei, and was awarded the Dean's Prize of Chinese Academy of Sciences. His research interests include information security, quantum information and wireless networks. He has authored or co-authored over 120 technical papers in major international journals and conferences. In 2014, he got the Natural Science Award of Ministry of Education of People's Republic of China. In 2016, he won the Best Paper Award at ACM UbiComp.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China under Grant No.62172385, and the Anhui Initiative in Quantum Information Technologies under Grant No.AHY150300.

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Serious price discrimination emerges with the development of big data and mobile networks, which harms the interests of consumers. To solve this problem, we propose a blockchain-based price consensus protocol to solve the malicious price discrimination faced by consumers. We give a mathematical definition of price discrimination, which requires the system to satisfy consistency and timeliness. The distributed blockchain can make the different pricing of merchants transparent to consumers, thus satisfying the consistency. The aging window mechanism of our protocol ensures that there is no disagreement between any node on the consensus on price or price discrimination within a fixed period, which meets the timeliness. Moreover, we evaluate its performance through a prototype implementation and experiments with up to 100 user nodes. Experimental results show that our protocol achieves all the expected goals like price transparency, consistency, and timeliness, and it additionally guarantees the consensus of the optimal price with a high probability.

Key words: blockchain; digital economy; distributed application; price discrimination; price transparency;

[1] Laffont J J, Rey P, Tirole J. Network competition: II. Price discrimination. The RAND Journal of Economics, 1998, 29(1): 38-56. DOI: 10.2307/2555815.
[2] Varian H R. Price discrimination. In Handbook of Industrial Organization, Schmalensee R, Willig R (eds.), North Halland, 1989, pp.597-654.
[3] Pigou A C. Discriminating monopoly. In The Economics of Welfare, Pigou A C, Aslanbeigui N (eds.), Routledge, 2002, pp.275-289. DOI: 10.4324/9781351304368.
[4] Belleflamme P, Peitz M. Group pricing and personalized pricing. In Industrial Organization: Markets and Strategies (2nd edition), Belleflamme P, Peitz M (eds.), Cambridge University Press, 2015, pp.197-219. DOI: 10.1017/CBO9781107707139.014.
[5] Rayna T, Darlington J, Striukova L. Pricing music using personal data: Mutually advantageous first-degree price discrimination. Electronic Markets, 2015, 25(2): 139-154. DOI: 10.1007/s12525-014-0165-7.
[6] Shiller B R. First degree price discrimination using big data. Technical Report, Department of Economics and International Business School, Brandeis University, 2014. https://www.brandeis.edu/economics/RePEc/brd/doc/Brandeis-WP58R2.pdf, Dec. 2020.
[7] Shiller B R. Personalized price discrimination using big data. Technical Report, Department of Economics and International Business School, Brandeis University, 2016, https://www.brandeis.edu/economics/RePEc/brd/doc/Br-andeis-WP108.pdf, Dec. 2020.
[8] Kshetri N. Big data's impact on privacy, security and consumer welfare. Telecommunications Policy, 2014, 38(11): 1134-1145. DOI: 10.1016/j.telpol.2014.10.002.
[9] Zhao Z. Big data price discrimination is repeated, why do ``ctrips'' choose to do evil? Business School, 2019, 177(12): 60-62. (in Chinese)
[10] Woodcock R A. Big data, price discrimination, and antitrust. Hastings Law Journal, 2017, 68(6): 1371-1420. DOI: 10.2139/ssrn.2817523.
[11] Steppe R. Online price discrimination and personal data: A general data protection regulation perspective. Computer Law & Security Review, 2017, 33(6): 768-785. DOI: 10.1016/j.clsr.2017.05.008.
[12] Varian H R. Price discrimination and social welfare. The American Economic Review, 1985, 75(4): 870-875.
[13] Adachi T. Third-degree price discrimination, consumption externalities and social welfare. Economica, 2005, 72(285): 171-178. DOI: 10.1111/j.0013-0427.2005.00407.x.
[14] Yoshida Y. Third-degree price discrimination in input markets: Output and welfare. American Economic Review, 2000, 90(1): 240-246. DOI: 10.1257/aer.90.1.240.
[15] Tirole J. The Theory of Industrial Organization (1st edition). MIT Press Books, 1988.
[16] Inderst R, Valletti T M. Buyer power and the ``waterbed effect''. The Journal of Industrial Economics, 2011, 59(1): 1-20. DOI: 10.1111/j.1467-6451.2011.00443.x.
[17] Kahneman D, Knetsch J L, Thaler R. Fairness as a constraint on profit seeking: Entitlements in the market. The American Economic Review, 1986, 76(4): 728-741.
[18] Baye M R, Morgan J, Scholten P. Price dispersion in the small and in the large: Evidence from an internet price comparison site. The Journal of Industrial Economics, 2004, 52(4): 463-496, DOI: 10.1111/j.0022-1821.2004.00236.x.
[19] Thomas R G. Non-risk price discrimination in insurance: Market outcomes and public policy. The Geneva Papers on Risk and Insurance---Issues and Practice, 2012, 37(1): 27-46. DOI: 10.1057/gpp.2011.32.
[20] Marks M, Marks J. Bidding method for Internet/wireless advertising and priority ranking in search results. https://www.freepatentsonline.com/20010051911.pdf, Dec. 2020.
[21] Jansen B J, Schuster S. Bidding on the buying funnel for sponsored search and keyword advertising. Journal of Electronic Commerce Research, 2011, 12(1): 1-18.
[22] Ellison G, Ellison S F. Search, obfuscation, and price elasticities on the Internet. Econometrica, 2010, 77(2): 427-452. DOI: 10.3982/ECTA5708.
[23] Garay J, Kiayias A, Leonardos N. The bitcoin backbone protocol with chains of variable difficulty. In Proc. the 37th Annual International Cryptology Conference, August 2017, pp.291-323. DOI: 10.1007/978-3-319-63688-7-10.
[24] Huckle S, Bhattacharya R, White M, Beloff N. Internet of things, blockchain and shared economy applications. Procedia Computer Science, 2016, 98: pp.461-466. DOI: 10.1016/j.procs.2016.09.074.
[25] Christidis K, Devetsikiotis M. Blockchains and smart contracts for the Internet of Things. IEEE Access, 2016, 4: 2292-2303. DOI: 10.1109/ACCESS.2016.2566339.
[26] Kshetri N. Can blockchain strengthen the Internet of Things? IEEE IT Professional, 2017, 19(4): 68-72. DOI: 10.1109/MITP.2017.3051335.
[27] Conoscenti M, Vetrò A, De Martin J C D. Blockchain for the Internet of Things: A systematic literature review. In Proc. the 13th IEEE/ACS International Conference of Computer Systems and Applications, November 29--December 2, 2016. DOI: 10.1109/AICCSA.2016.7945805.
[28] Dorri A, Kanhere S S, Jurdak R, Gauravaram P. Blockchain for IoT security and privacy: The case study of a smart home. In Proc. the 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), March 2017, pp.618-623. DOI: 0.1109/PERCOMW.2017.7917634.
[29] Sharma V. An energy-efficient transaction model for the blockchain-enabled Internet of Vehicles (IoV). IEEE Communications Letters, 2019, 23(2): 246-249. DOI: 10.1109/LCOMM.2018.2883629.
[30] Liu H, Zhang Y, Yang T. Blockchain-enabled security in electric vehicles cloud and edge computing. IEEE Network, 2018, 32(3): 78-83. DOI: 10.1109/MNET.2018.1700344.
[31] Jiang T, Fang H, Wang H. Blockchain-based Internet of Vehicles: Distributed network architecture and performance analysis. IEEE Internet of Things Journal, 2019, 6(3): 4640-4649. DOI: 10.1109/JIOT.2018.2874398.
[32] Kang J, Xiong Z, Niyato D, Ye D, Kim D I, Zhao J. Toward secure blockchain-enabled Internet of Vehicles: Optimizing consensus management using reputation and contract theory. IEEE Trans. Vehicular Technology, 2019, 68(3): 2906-2920. DOI: 10.1109/TVT.2019.2894944.
[33] Weng J, Weng J, Li M, Zhang Y, Luo W. DeepChain: Auditable and privacy-preserving deep learning with blockchain-based incentive. IEEE Transactions on Dependable and Secure Computing, 2018, 18(5): 2438-2455. DOI: 10.1109/TDSC.2019.2952332.
[34] Cheng K, Fan T, Jin Y, Liu Y, Chen T, Yang Q. Secure-boost: A lossless federated learning framework. arXiv:1901.08755, 2019. http://arxiv.org/abs/1901.08755, Dec. 2020.
[35] Zhuo H H, Feng W, Xu Q, Yang Q, Lin Y. Federated reinforcement learning. arXiv:1901.08277, 2019. http://arxiv.org/abs/1901.08277, Dec. 2020.
[36] Sompolinsky Y, Zohar A. PHANTOM: A scalable blockDAG protocol. http://eprint.iacr.org/2018/104, Dec. 2020.
[37] Xiao S, Wang X A, Wang H. Large-scale electronic voting based on conflux consensus mechanism. In Proc. the 13th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, July 2019, pp.291-299. DOI: 10.1007/978-3-030-22263-5-28.
[38] Andrychowicz M, Dziembowski S. PoW-based distributed cryptography with no trusted setup. In Proc. the 35th Annual Cryptology Conference, August 2015, pp.379-399. DOI: 10.1007/978-3-662-48000-7-19.
[39] Gilad Y, Hemo R, Micali S et al. Algorand: Scaling Byzantine agreements for cryptocurrencies. In Proc. the 26th Symposium on Operating Systems Principles, October 2017, pp.51-68. DOI: 10.1145/3132747.3132757.
[40] Lamport L, Shostak R, Pease M. The Byzantine generals problem. ACM Trans. Programming Languages and Systems, 1982, 4(3): 382-401. DOI: 10.1145/357172.357176.
[41] Eyal I, Gencer A E, Sirer E G et al. Bitcoin-NG: A scalable blockchain protocol. In Proc. the 13th USENIX Symposium on Networked Systems Design and Implementation, March 2016, pp.45-59.
[42] Kogias E K, Jovanovic P, Gailly N et al. Enhancing bitcoin security and performance with strong consistency via collective signing. In Proc. the 25th USENIX Security Symposium, August 2016, pp.279-296.
[43] Schossmaier K, Schmid U, Horauer M, Loy D. Specification and implementation of the universal time coordinated synchronization unit (UTCSU). Real-Time Systems, 1997, 12(3): 295-327. DOI: 10.1023/A:1007953214631.
[44] Schmid U. Synchronized universal time coordinated for distributed real-time systems. Control Engineering Practice, 1995, 3(6): 877-884. DOI: 10.1016/0967-0661(95)00073-4.
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[1] Min Yinghua;. Easy Test Generation PLAs[J]. , 1987, 2(1): 72 -80 .
[2] Sun Yongqiang; Lu Ruzhan; Huang Xiaorong;. Termination Preserving Problem in the Transformation of Applicative Programs[J]. , 1987, 2(3): 191 -201 .
[3] Shi Weigeng; StephenY.H.Su;. An Online Diagnosable Fault-Tolerant Redundancy System[J]. , 1987, 2(4): 310 -321 .
[4] Feng Yulin;. Hierarchical Protocol Analysis by Temporal Logic[J]. , 1988, 3(1): 56 -69 .
[5] Yao Rong; Kang Tai; Chen Tinghuai;. Algorithms for the Determination of Cutsets in a Hypergraph[J]. , 1990, 5(1): 41 -46 .
[6] Zhu Mingyuan;. Two Congruent Semantics for Prolog with CUT[J]. , 1990, 5(1): 82 -91 .
[7] Xu Jie; Li Qingnan; Huang Shize; Xu Jiangfeng;. DFTSNA:A Distributed Fault-Tolerant Shipboard System[J]. , 1990, 5(2): 109 -116 .
[8] Li Jintao; Min Yinghua;. Product-Oriented Test-Pattern Generation for Programmable Logic Arrays[J]. , 1990, 5(2): 164 -174 .
[9] Shen Li;. Testability Analysis at Switch Level for CMOS Circuits[J]. , 1990, 5(2): 197 -202 .
[10] Han Jianchao; Shi Zhongzhi;. Formalizing Default Reasoning[J]. , 1990, 5(4): 374 -378 .

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