  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.
