Abstract: We study the role of information asymmetries in first-price common value auctions, and how different information structures provide revenue opportunities to third-party information brokers. One of our motivations is Internet ad auctions, where ads for specific site visitors have a common-value nature, in that displaying an ad to a bot has no value, and users are likely to carry the same (or similar) value to different advertisers. Cookies significantly improve the estimate of this value; the fact that different advertisers use cookies to different extents (and use cookies of different qualities) thus leads to significant information asymmetries. These asymmetries can be mitigated or exacerbated when the advertisers obtain cookie information from third-party information brokers. We focus on a first-price common value auction in which bidders have access to independent discrete signals about the value of an item, which is either 0 or 1. Our first main result is that with two bidders, this auction format has a unique Nash equilibrium; we give a linear-time algorithm for computing this equilibrium and the resulting value of the auction for each player. We then use this equilibrium calculation to understand the value of additional information to the bidders. Third-party information has the interesting property that it can (in principle) be allocated to multiple bidders, and that it carries significant externalities. We find that the value of additional information is subtle, and depends on the prior and the information already available. We exhibit several surprising possibilities including (1) Additional information about the item does not always result in higher value for a bidder. (2) A bidder may prefer his competitor (but not himself) to receive the broker's signal; this applies both to the more and less informed bidder. Bio: David Kempe received his Ph.D. from Cornell University in 2003, and has been on the faculty in the computer science department at USC since the Fall of 2004, where he is currently an Associate Professor. His primary research interests are in computer science theory and the design and analysis of algorithms, with a particular emphasis on social networks, algorithms for feature selection, and game-theoretic and pricing questions. He is a recipient of the NSF CAREER award, the VSoE Junior Research Award, the ONR Young Investigator Award, and a Sloan Fellowship, in addition to several USC mentoring awards.