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1. Ibex: Privacy-preserving Ad Conversion Tracking and Bidding

   https://doi.org/10.1145/3548606.3560651  

   Zhong, Ke ; Ma, Yiping ; Angel, Sebastian ( November 2022 , AACM SIGSAC Conference on Computer and Communications Security)

   This paper introduces Ibex, an advertising system that reduces the amount of data that is collected on users while still allowing advertisers to bid on real-time ad auctions and measure the effectiveness of their ad campaigns. Specifically, Ibex addresses an issue in recent proposals such as Google’s Privacy Sandbox Topics API in which browsers send information about topics that are of interest to a user to advertisers and demand-side platforms (DSPs). DSPs use this information to (1) determine how much to bid on the auction for a user who is interested in particular topics, and (2) measure how well their ad campaign does for a given audience (i.e., measure conversions). While Topics and related proposals reduce the amount of user information that is exposed, they still reveal user preferences. In Ibex, browsers send user information in an encrypted form that still allows DSPs and advertisers to measure conversions, compute aggregate statistics such as histograms about users and their interests, and obliviously bid on auctions without learning for whom they are bidding. Our implementation of Ibex shows that creating histograms is 1.7–2.5× more expensive for browsers than disclosing user information, and Ibex’s oblivious bidding protocol can finish auctions within 550 ms.more »We think this makes Ibex capable of preserving a good experience while improving user privacy.« less
2. Incentive-Compatible Learning of Reserve Prices for Repeated Auctions

   https://doi.org/10.1287/opre.2020.2007  

   Kanoria, Yash ; Nazerzadeh, Hamid ( March 2021 , Operations Research)

   Large fractions of online advertisements are sold via repeated second-price auctions. In these auctions, the reserve price is the main tool for the auctioneer to boost revenues. In this work, we investigate the following question: how can the auctioneer optimize reserve prices by learning from the previous bids while accounting for the long-term incentives and strategic behavior of the bidders? To this end, we consider a seller who repeatedly sells ex ante identical items via a second-price auction. Buyers’ valuations for each item are drawn independently and identically from a distribution F that is unknown to the seller. We find that if the seller attempts to dynamically update a common reserve price based on the bidding history, this creates an incentive for buyers to shade their bids, which can hurt revenue. When there is more than one buyer, incentive compatibility can be restored by using personalized reserve prices, where the personal reserve price for each buyer is set using the historical bids of other buyers. Such a mechanism asymptotically achieves the expected revenue obtained under the static Myerson optimal auction for F. Further, if valuation distributions differ across bidders, the loss relative to the Myerson benchmark is only quadratic inmore »the size of such differences. We extend our results to a contextual setting where the valuations of the buyers depend on observed features of the items. When up-front fees are permitted, we show how the seller can determine such payments based on the bids of others to obtain an approximately incentive-compatible mechanism that extracts nearly all the surplus.« less
3. Robust Auctions for Revenue via Enhanced Competition

   https://doi.org/10.1287/opre.2019.1929  

   Roughgarden, Tim ; Talgam-Cohen, Inbal ; Yan, Qiqi ( July 2020 , Operations Research)

   Most results in revenue-maximizing mechanism design hinge on “getting the price right”—selling goods to bidders at prices low enough to encourage a sale but high enough to garner nontrivial revenue. This approach is difficult to implement when the seller has little or no a priori information about bidder valuations or when the setting is sufficiently complex, such as matching markets with heterogeneous goods. In this paper, we apply a robust approach to designing auctions for revenue. Instead of relying on prior knowledge regarding bidder valuations, we “let the market do the work” and let prices emerge from competition for scarce goods. We analyze the revenue guarantees of one of the simplest imaginable implementations of this idea: first, we enhance competition in the market by increasing demand (or alternatively, by limiting supply), and second, we run a standard second price (Vickrey) auction. In their renowned work from 1996 , Bulow and Klemperer [Bulow J, Klemperer P (1996) Auctions vs. negotiations. Amer. Econom. Rev. 86(1):180–194.] apply this method to markets with single goods. As our main result, we give the first application beyond single-parameter settings, proving that, simultaneously for many valuation distributions, this method achieves expected revenue at least as good asmore »the optimal revenue in the original market. Our robust and simple approach provides a handle on the elusive optimal revenue in multiitem matching markets and shows when the use of welfare-maximizing Vickrey auctions is justified, even if revenue is a priority. By establishing quantitative tradeoffs, our work provides guidelines for a seller in choosing among two different revenue-extracting strategies: sophisticated pricing based on market research or advertising to draw additional bidders.« less
4. When Push Comes to Ads: Measuring the Rise of (Malicious) Push Advertising

   https://doi.org/10.1145/3419394.3423631  

   Subramani, Karthika ; Yuan, Xingzi ; Setayeshfar, Omid ; Vadrevu, Phani ; Lee, Kyu Hyung ; Perdisci, Roberto ( October 2020 , Proceedings of the 2020 ACM Internet Measurement Conference (IMC))

   The rapid growth of online advertising has fueled the growth of ad-blocking software, such as new ad-blocking and privacy-oriented browsers or browser extensions. In response, both ad publishers and ad networks are constantly trying to pursue new strategies to keep up their revenues. To this end, ad networks have started to leverage the Web Push technology enabled by modern web browsers. As web push notifications (WPNs) are relatively new, their role in ad delivery has not yet been studied in depth. Furthermore, it is unclear to what extent WPN ads are being abused for malvertising (i.e., to deliver malicious ads). In this paper, we aim to fill this gap. Specifically, we propose a system called PushAdMiner that is dedicated to (1) automatically registering for and collecting a large number of web-based push notifications from publisher websites, (2) finding WPN-based ads among these notifications, and (3) discovering malicious WPN-based ad campaigns. Using PushAdMiner, we collected and analyzed 21,541 WPN messages by visiting thousands of different websites. Among these, our system identified 572 WPN ad campaigns, for a total of 5,143 WPN-based ads that were pushed by a variety of ad networks. Furthermore, we found that 51% of all WPN ads wemore »collected are malicious, and that traditional ad-blockers and URL filters were mostly unable to block them, thus leaving a significant abuse vector unchecked.« less
5. The Strange Role of Information Asymmetry in Auctions—Does More Accurate Value Estimation Benefit a Bidder?

   https://doi.org/10.1609/aaai.v36i5.20459  

   Xu, Haifeng ; Cavallo, Ruggiero ( June 2022 , Proceedings of the AAAI Conference on Artificial Intelligence)

   We study the second-price auction in which bidders have asymmetric information regarding the item’s value. Each bidder’s value for the item depends on a private component and a public component. While each bidder observes their own private component, they hold different and asymmetric information about the public component. We characterize the equilibrium of this auction game and study how the asymmetric bidder information affects their equilibrium bidding strategies. We also discover multiple surprisingly counter-intuitive equilibrium phenomena. For instance, a bidder may be better off if she is less informed regarding the public component. Conversely, a bidder may sometimes be worse off if she obtains more accurate estimation about the auctioned item. Our results suggest that efforts devoted by bidders to improve their value estimations, as widely seen in today’s online advertising auctions, may not always be to their benefit.