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Who is Fiddling with Prices?: Building and Deploying a Watchdog Service for E-commerce

Published:07 August 2017Publication History

ABSTRACT

We present the design, implementation, validation, and deployment of the Price Sheriff, a highly distributed system for detecting various types of online price discrimination in e-commerce. The Price Sheriff uses a peer-to-peer architecture, sandboxing, and secure multiparty computation to allow users to tunnel price check requests through the browsers of other peers without tainting their local or server-side browsing history and state. Having operated the Price Sheriff for several months with approximately one thousand real users, we identify several instances of cross-border price discrimination based on the country of origin. Even within national borders, we identify several retailers that return different prices for the same product to different users. We examine whether the observed differences are due to personal-data-induced discrimination or A/B testing, and conclude that it is the latter.

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References

  1. 2015. AMT - Amazon Mechanical Turk. https://www.mturk.com. (2015).Google ScholarGoogle Scholar
  2. 2015. PhantomJS - Headless Web Browser. http://phantomjs.org/. (2015).Google ScholarGoogle Scholar
  3. 2017. Google Chrome Extension JavaScript APIs. https://developer.chrome.com/extensions/api_index. (2017).Google ScholarGoogle Scholar
  4. 2017. Monzilla Web Extension JavaScript APIs. https://developer.mozilla.org/en-US/Add-ons/WebExtensions/API. (2017).Google ScholarGoogle Scholar
  5. 2017. Price Optimization Strategies. http://www.pros.com/solutions/price-optimization-software. (2017).Google ScholarGoogle Scholar
  6. 2017. The Onion Router. https://www.torproject.org/. (2017).Google ScholarGoogle Scholar
  7. Michel Abdalla, Florian Bourse, Angelo De Caro, and David Pointcheval. 2015. Simple Functional Encryption Schemes for Inner Products. In Proc. of Public-Key Cryptography (PKC). 733--751. Google ScholarGoogle ScholarCross RefCross Ref
  8. Gunes Acar, Christian Eubank, Steven Englehardt, Marc Juarez, Arvind Narayanan, and Claudia Diaz. 2014. The Web Never Forgets: Persistent Tracking Mechanisms in the Wild. In Proc. of ACM Conference on Computer and Communications Security (CCS). 674--689. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Gunes Acar, Marc Juarez, Nick Nikiforakis, Claudia Diaz, Seda Gürses, Frank Piessens, and Bart Preneel. 2013. FPDetective: Dusting the Web for Fingerprinters. In Proc. of ACM Conference on Computer and Communications Security (CCS). 1129--1140. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Anonymous. 2017. Who is Fiddling with Prices? Building and Deploying a Watchdog Service for E-commerce. Technical Report. https://goo.gl/4p0Ft9. (2017). https://goo.gl/4p0Ft9Google ScholarGoogle Scholar
  11. Solon Barocas. 2014. Data Mining and the Discourse on Discrimination. In Proc. of Data Ethics Workshop of KDD.Google ScholarGoogle Scholar
  12. Assaf Ben-David, Noam Nisan, and Benny Pinkas. 2008. FairplayMP: a System for Secure Multi-party Computation. In Proc. of ACM Computer and Communications Security (CCS). 257--266. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Le Chen, Alan Mislove, and Christo Wilson. 2016. An Empirical Analysis of Algorithmic Pricing on Amazon Marketplace. In Proceedings of the 25th International World Wide Web Conference (WWW 2016). MontrÃl'al, Canada.Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Taher El Gamal. 1984. A Public Key Cryptosystem and a Signature Scheme Based on Discrete Logarithms. In CRYPTO. 10--18.Google ScholarGoogle Scholar
  15. Aniko Hannak, Gary Soeller, David Lazer, Alan Mislove, and Christo Wilson. 2014. Measuring Price Discrimination and Steering on E-commerce Web Sites. In Proc. of USENIX/ACM Internet Measurement Conference (IMC). Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Wilko Henecka, Stefan Kögl, Ahmad-Reza Sadeghi, Thomas Schneider, and Immo Wehrenberg. 2010. TASTY: Tool for Automating Secure Two-party Computations. In Proc. of ACM Computer and Communications Security (CCS). 451--462. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Jakub Mikians, László Gyarmati, Vijay Erramilli, and Nikolaos Laoutaris. 2012. Detecting Price and Search Discrimination on the Internet. In Proc. of Workshop on Hot Topics in Networks (HotNets). Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Jakub Mikians, László Gyarmati, Vijay Erramilli, and Nikolaos Laoutaris. 2013. Crowd-assisted Search for Price Discrimination in e-Commerce: First Results. In Proc. of Conference on Emerging Networking Experiments and Technologies (CoNEXT). Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Andrew Odlyzko. 2003. Privacy, economics, and price discrimination on the Internet. In Proc. International Conference on Electronic Commerce (ICEC). Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Fotios Papaodyssefs, Costas Iordanou, Jeremy Blackburn, Nikolaos Laoutaris, and Konstantina Papagiannaki. 2015. Web Identity Translator: Behavioral Advertising and Identity Privacy with WIT. In Proc. of Workshop on Hot Topics in Networks (HotNets). ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Peter J. Rousseeuw. 1987. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20 (1987), 53--65. http://www.sciencedirect.com/science/article/pii/0377042787901257 Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Michael Sinkinson and Katja Seim. 2015. Mixed Pricing in Online Marketplaces. Work in Progress, http://assets.wharton.upenn.edu/~msink/mixed_pricing.pdf. (2015).Google ScholarGoogle Scholar
  23. Thomas Vissers, Nick Nikiforakis, Nataliia Bielova, and Wouter Joosen. 2014. Crying Wolf? On the Price Discrimination of Online Airline Tickets. In Proc. of Workshop on Hot Topics in Privacy Enhancing Technologies (HotPETs).Google ScholarGoogle Scholar

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            cover image ACM Conferences
            SIGCOMM '17: Proceedings of the Conference of the ACM Special Interest Group on Data Communication
            August 2017
            515 pages
            ISBN:9781450346535
            DOI:10.1145/3098822

            Copyright © 2017 ACM

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            Publication History

            • Published: 7 August 2017

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            Overall Acceptance Rate554of3,547submissions,16%

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