2024/05/17 by Arash Jamshidi, Jamshidi, Arash, Seyed Mohammad Hosseini +5
Computer Science · Decision Sciences · #Auction Theory and Applications #Blockchain Technology Applications and Security #Computer Science and Game Theory (cs.GT) #Cryptography and Data Security #FOS: Computer and information sciences #Information Theory (cs.IT)
paper · pdf · doi:10.48550/arxiv.2405.10622
openalex publication_date 2024/05/17 · openalex created_date 2024/05/21 · openalex updated_date 2026/07/28
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.