  Setting an effective reserve price for strategic bidders in repeated auctions
is a central question in online advertising. In this paper, we investigate how
to set an anonymous reserve price in repeated auctions based on historical bids
in a way that balances revenue and incentives to misreport. We propose two
simple and computationally efficient methods to set reserve prices based on the
notion of a clearing price and make them robust to bidder misreports. The first
approach adds random noise to the reserve price, drawing on techniques from
differential privacy. The second method applies a smoothing technique by adding
noise to the training bids used to compute the reserve price. We provide
theoretical guarantees on the trade-offs between the revenue performance and
bid-shading incentives of these two mechanisms. Finally, we empirically
evaluate our mechanisms on synthetic data to validate our theoretical findings.
