This paper studies the problem of designing near-optimal repeated auctions with the goal of maximizing revenue for the seller. The classical approach to optimizing revenue requires a known prior distribution on the demand of the participants in the auction, but this can be replaced with a sequential learning strategy that estimates bidder demand from data. When buyers can participate in multiple rounds, however, they have an opportunity and incentive to strategically adjust their behavior, especially in earlier rounds, to achieve more favorable prices. Such non-truthful behavior is undesirable for many reasons, and in this work we propose a repeated auction framework that is approximately revenue optimal yet limits the bidders’ potential for manipulation. The fundamental tool we rely on is differential privacy, which helps to ensure weak dependencies in the mechanism from round to round.