Sponsored search auctions form a multibillion dollar industry. Search providers auction advertisement slots on search result pages to advertisers who are charged only if the end-user clicks on the advertiser's ad. The high volume of searches presents an opportunity for sharing the workrequired to resolve multiple auctions that occur simultaneously. We provide techniques for efficiently resolving sponsored search auctions involving large numbers of advertisers, with a focus on two issues: sharing work between multiple search auctions using shared aggregation and shared sort, and dealing with budget uncertainty arising from ads that have been displayed from previous auctions but have not received clicks yet.

Internet search results are a growing and highly profitable advertising platform. Search providers auction advertising slots to advertisers on their search result pages. Due to the high volume of searches and the users' low tolerance for search result latency, it is imperative to resolve these auctions fast. Current approaches restrict the expressiveness of bids in order to achieve fast winner determination, which is the problem of allocating slots to advertisers so as to maximize the expected revenue given the advertisers' bids. The goal of our work is to permit more expressive bidding, thus allowing advertisers to achieve complex advertising goals, while still providing fast and scalable techniques for winner determination.

Recent work has shown the necessity of considering an attacker's background knowledge when reasoning about privacy in data publishing. However, in practice, the data publisher does not know what background knowledge the attacker possesses. Thus, it is important to consider the worst-case. In this paper, we initiate a formal study of worst-case background knowledge. We propose a language that can express any background knowledge about the data. We provide a polynomial time algorithm to measure the amount of disclosure of sensitive information in the worst case, given that the attacker has at most k pieces of information in this language. We also provide a method to efficiently sanitize the data so that the amount of disclosure in the worst case is less than a specified threshold.