Abstract Ad exchanges are kind of the most popular online advertising marketplaces for trading ad spaces over the Internet. Ad exchanges run auctions to sell diverse ad spaces on the publishers’ web-pages to advertisers, who want to display ads on ad spaces. However, the parties in an ad auction cannot verify whether the auction is carried out correctly or not. Furthermore, the advertisers are usually unwilling to reveal their sensitive bids and identities. In this paper, we jointly consider the auction verifiability and the advertisers’ privacy preservation, and thus propose ERA, which is an E fficient, p R ivacy-preserving, and verifi A ble online auction mechanism for ad exchanges. ERA exploits an order preserving encryption scheme to guarantee privacy preservation, and achieves verifiability by constructing a novel protocol of privacy preserving integer comparison, which is built on the Paillier homomorphic encryption scheme. We extensively evaluate the performance of ERA, and our evaluation results show that ERA satisfies several desirable properties with low computation, communication, and storage overheads, so ERA can be easily deployed in today's ad exchanges.
When these existing privacy preserving and or verifiable auction mechanisms are directly applied to ad exchanges, they have high computation and communication complexity. Thus, these works could only support auctions with small scale @cite_40 @cite_44 @cite_42 or a limited number of possible bids @cite_3 @cite_22 @cite_37 , which may be unacceptable for today's ad exchanges.
@cite_37 @cite_22 @cite_42 @cite_3 @cite_44 @cite_40
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This paper treats a critical component of the Web ecosystem that has so far received little attention in our community: ad exchanges. Ad exchanges run auctions to sell publishers' inventory-space on Web pages-to advertisers who want to display ads in those spaces. Unfortunately, under the status quo, the parties to an auction cannot check that the auction was carried out correctly, which raises the following more general question: how can we create verifiability in low-latency, high-frequency auctions where the parties do not know each other? We address this question with the design, prototype implementation, and experimental evaluation of VEX. VEX introduces a technique for efficient, privacy-preserving integer comparisons; couples these with careful protocol design; and adds little latency and tolerable overhead. We investigate whether it is possible to preserve privacy in sealed-bid auctions to a maximal extent. In particular, this paper focuses on unconditional full privacy , i.e., privacy that relies neither on trusted third parties (like auctioneers), nor on computational intractability assumptions (like the hardness of factoring). These constraints imply a scenario in which bidders exchange messages according to some predefined protocol in order to jointly determine the auction outcome without revealing any additional information. It turns out that the first-price sealed-bid auction can be emulated by an unconditionally fully private protocol. However, the protocol's round complexity is exponential in the bid size, and there is no more efficient protocol. On the other hand, we prove the impossibility of privately emulating the second-price sealed-bid auction for more than two bidders. This impossibility holds even when relaxing various privacy constraints such as allowing the revelation of all but one losing bid (while maintaining anonymity) or allowing the revelation of the second highest bidder's identity. We study the role that privacy-preserving algorithms, which prevent the leakage of specific information about participants, can play in the design of mechanisms for strategic agents, which must encourage players to honestly report information. Specifically, we show that the recent notion of differential privacv, in addition to its own intrinsic virtue, can ensure that participants have limited effect on the outcome of the mechanism, and as a consequence have limited incentive to lie. More precisely, mechanisms with differential privacy are approximate dominant strategy under arbitrary player utility functions, are automatically resilient to coalitions, and easily allow repeatability. We study several special cases of the unlimited supply auction problem, providing new results for digital goods auctions, attribute auctions, and auctions with arbitrary structural constraints on the prices. As an important prelude to developing a privacy-preserving auction mechanism, we introduce and study a generalization of previous privacy work that accommodates the high sensitivity of the auction setting, where a single participant may dramatically alter the optimal fixed price, and a slight change in the offered price may take the revenue from optimal to zero. Ads on the Internet are increasingly sold via ad exchanges such as RightMedia, AdECN and Doubleclick Ad Exchange. These exchanges allow real-time bidding, that is, each time the publisher contacts the exchange, the exchange "calls out" to solicit bids from ad networks. This solicitation introduces a novel aspect, in contrast to existing literature. This suggests developing a joint optimization framework which optimizes over the allocation and well as solicitation. We model this selective call out as an online recurrent Bayesian decision framework with bandwidth type constraints. We obtain natural algorithms with bounded performance guarantees for several natural optimization criteria. We show that these results hold under different call out constraint models, and different arrival processes. Interestingly, the paper shows that under MHR assumptions, the expected revenue of generalized second price auction with reserve is constant factor of the expected welfare. Also the analysis herein allow us prove adaptivity gap type results for the adwords problem.