Differentially Private Machine Learning-powered Combinatorial Auction Design
Abstract: We present a new approach to machine learning-powered combinatorial auctions, which is based on the principles of Differential Privacy. Our methodology guarantees that the auction mechanism is truthful, meaning that rational bidders have the incentive to reveal their true valuation functions. We achieve this by inducing truthfulness in the auction dynamics, ensuring that bidders consistently provide accurate information about their valuation functions. Our method not only ensures truthfulness but also preserves the efficiency of the original auction. This means that if the initial auction outputs an allocation with high social welfare, our modified truthful version of the auction will also achieve high social welfare. We use techniques from Differential Privacy, such as the Exponential Mechanism, to achieve these results. Additionally, we examine the application of differential privacy in auctions across both asymptotic and non-asymptotic regimes.
- G. Brero, B. Lubin, and S. Seuken, “Machine Learning-powered Iterative Combinatorial Auctions,” Sep. 2021, arXiv:1911.08042 [cs]. [Online]. Available: http://arxiv.org/abs/1911.08042
- M. Beyeler, G. Brero, B. Lubin, and S. Seuken, “imlca: Machine learning-powered iterative combinatorial auctions with interval bidding,” in Proceedings of the 22nd ACM Conference on Economics and Computation, 2021, pp. 136–136.
- A. Ghosh and A. Roth, “Selling privacy at auction,” in Proceedings of the 12th ACM conference on Electronic commerce, 2011, pp. 199–208.
- C. Li, D. Y. Li, G. Miklau, and D. Suciu, “A theory of pricing private data,” ACM Transactions on Database Systems (TODS), vol. 39, no. 4, pp. 1–28, 2014.
- K. Nissim, S. Vadhan, and D. Xiao, “Redrawing the boundaries on purchasing data from privacy-sensitive individuals,” in Proceedings of the 5th conference on Innovations in theoretical computer science, 2014, pp. 411–422.
- K. Nissim, R. Smorodinsky, and M. Tennenholtz, “Approximately optimal mechanism design via differential privacy,” in Proceedings of the 3rd innovations in theoretical computer science conference, 2012, pp. 203–213.
- F. McSherry and K. Talwar, “Mechanism design via differential privacy,” in 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS’07). IEEE, 2007, pp. 94–103.
- Z. Huang and S. Kannan, “The exponential mechanism for social welfare: Private, truthful, and nearly optimal,” in 2012 IEEE 53rd Annual Symposium on Foundations of Computer Science. IEEE, 2012, pp. 140–149.
- J. Hsu, Z. Huang, A. Roth, and Z. S. Wu, “Jointly private convex programming,” in Proceedings of the twenty-seventh annual ACM-SIAM symposium on Discrete algorithms. SIAM, 2016, pp. 580–599.
- P. Milgrom, “PUTTING AUCTION THEORY TO WORK,” 2003.
- W. Vickrey, “Counterspeculation, Auctions, and Competitive Sealed Tenders,” The Journal of Finance, vol. 16, no. 1, pp. 8–37, 1961, publisher: [American Finance Association, Wiley]. [Online]. Available: https://www.jstor.org/stable/2977633
- E. H. Clarke, “Multipart pricing of public goods,” Public Choice, vol. 11, no. 1, pp. 17–33, Sep. 1971. [Online]. Available: http://link.springer.com/10.1007/BF01726210
- T. Groves, “Incentives in Teams,” Econometrica, vol. 41, no. 4, p. 617, Jul. 1973. [Online]. Available: https://www.jstor.org/stable/1914085?origin=crossref
- C. Dwork and A. Roth, “The Algorithmic Foundations of Differential Privacy,” Foundations and Trends® in Theoretical Computer Science, vol. 9, no. 3-4, pp. 211–407, 2013. [Online]. Available: http://www.nowpublishers.com/articles/foundations-and-trends-in-theoretical-computer-science/TCS-042
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