The repeated nature of sponsored search auctions allows the seller to implement Myerson’s auction to maximize revenue using past data. But since these data are provided by strategic buyers in the auctions, they can be manipulated, which may hurt the seller’s revenue. We model this problem as a Private Data Manipulation (PDM) game: the seller first announces an auction (such as Myerson’s) whose allocation and payment rules depend on the value distributions of buyers; the buyers then submit fake value distributions to the seller to implement the auction. The seller’s expected revenue and the buyers’ expected utilities depend on the auction rule and the game played among the buyers in their choices of the submitted distributions. Under the PDM game, we show that Myerson’s auction is equivalent to the generalized first-price auction, and under further assumptions equivalent to the Vickrey–Clarke–Groves (VCG) auction and the generalized second-price auction. Our results partially explain why Myerson’s auction is not as popular as the generalized second-price auction in the practice of sponsored search auctions, and provide new perspectives into data-driven decision making in mechanism design.