Skip to main content
Log in

A Blockchain-Based Protocol for Malicious Price Discrimination

  • Regular Paper
  • Published:
Journal of Computer Science and Technology Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Laffont J J, Rey P, Tirole J. Network competition: II. Price discrimination. The RAND Journal of Economics, 1998, 29(1): 38-56. https://doi.org/10.2307/2555815.

    Article  Google Scholar 

  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. https://doi.org/10.4324/9781351304368.

  4. Belleamme P, Peitz M. Group pricing and personalized pricing. In Industrial Organization: Markets and Strategies (2nd edition), Belleamme P, Peitz M (eds.), Cambridge University Press, 2015, pp.197-219. https://doi.org/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. https://doi.org/10.1007/s12525-014-0165-7.

    Article  Google Scholar 

  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/Brandeis WP108.pdf, Dec. 2020.

  8. Kshetri N. Big data’s impact on privacy, security and consumer welfare. Telecommunications Policy, 2014, 38(11): 1134-1145. https://doi.org/10.1016/j.telpol.2014.10.002.

    Article  Google Scholar 

  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)

    Google Scholar 

  10. Woodcock R A. Big data, price discrimination, and antitrust. Hastings Law Journal, 2017, 68(6): 1371-1420. https://doi.org/10.2139/ssrn.2817523.

    Article  Google Scholar 

  11. Steppe R. Online price discrimination and personal data: A general data protection regulation perspective. Computer Law & Security Review, 2017, 33(6): 768-785. https://doi.org/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.

    Google Scholar 

  13. Adachi T. Third-degree price discrimination, consumption externalities and social welfare. Economica, 2005, 72(285): 171-178. https://doi.org/10.1111/j.0013-0427.2005.00407.x.

    Article  Google Scholar 

  14. Yoshida Y. Third-degree price discrimination in input markets: Output and welfare. American Economic Review, 2000, 90(1): 240-246. https://doi.org/10.1257/aer.90.1.240.

    Article  Google Scholar 

  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. https://doi.org/10.1111/j.1467-6451.2011.00443.x.

    Article  Google Scholar 

  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.

    Google Scholar 

  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, https://doi.org/10.1111/j.0022-1821.2004.00236.x.

    Article  Google Scholar 

  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. https://doi.org/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.

    Google Scholar 

  22. Ellison G, Ellison S F. Search, obfuscation, and price elasticities on the Internet. Econometrica, 2010, 77(2): 427-452. https://doi.org/10.3982/ECTA5708.

    Article  MATH  Google Scholar 

  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. https://doi.org/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. https://doi.org/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. https://doi.org/10.1109/ACCESS.2016.2566339.

    Article  Google Scholar 

  26. Kshetri N. Can blockchain strengthen the Internet of Things? IEEE IT Professional, 2017, 19(4): 68-72. https://doi.org/10.1109/MITP.2017.3051335.

    Article  Google Scholar 

  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. https://doi.org/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. https://doi.org/10.1109/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. https://doi.org/10.1109/LCOMM.2018.2883629.

    Article  Google Scholar 

  30. Liu H, Zhang Y, Yang T. Blockchain-enabled security in electric vehicles cloud and edge computing. IEEE Network, 2018, 32(3): 78-83. https://doi.org/10.1109/MNET.2018.1700344.

    Article  Google Scholar 

  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. https://doi.org/10.1109/JIOT.2018.2874398.

    Article  Google Scholar 

  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. https://doi.org/10.1109/TVT.2019.2894944.

    Article  Google Scholar 

  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. https://doi.org/10.1109/TDSC.2019.2952332.

    Article  Google Scholar 

  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 conux consensus mechanism. In Proc. the 13th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, July 2019, pp.291-299. 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. https://doi.org/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. https://doi.org/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. https://doi.org/10.1145/357172.357176.

    Article  MATH  Google Scholar 

  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. https://doi.org/10.1023/A:1007953214631.

    Article  Google Scholar 

  44. Schmid U. Synchronized universal time coordinated for distributed real-time systems. Control Engineering Practice, 1995, 3(6): 877-884. https://doi.org/10.1016/0967-0661(95)00073-4.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wei Yang.

Supplementary Information

ESM 1

(PDF 128 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xue, LD., Liu, YJ., Yang, W. et al. A Blockchain-Based Protocol for Malicious Price Discrimination. J. Comput. Sci. Technol. 37, 266–276 (2022). https://doi.org/10.1007/s11390-021-0583-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11390-021-0583-x

Keywords

Navigation