The internet advertising market is a multibillion dollar industry in which advertisers buy thousands of ad placements every day by repeatedly participating in auctions. An important and ubiquitous feature of these auctions is the presence of campaign budgets, which specify the maximum amount the advertisers are willing to pay over a specified time period. In this paper, we present a new model to study the equilibrium bidding strategies in standard auctions, a large class of auctions that includes first and second price auctions, for advertisers who satisfy budget constraints on average. Our model dispenses with the common yet unrealistic assumption that advertisers’ values are independent and instead assumes a contextual model in which advertisers determine their values using a common feature vector. We show the existence of a natural value pacing–based Bayes–Nash equilibrium under very mild assumptions. Furthermore, we prove a revenue equivalence showing that all standard auctions yield the same revenue even in the presence of budget constraints. Leveraging this equivalence, we prove price of anarchy bounds for liquid welfare and structural properties of pacing-based equilibria that hold for all standard auctions. In recent years, the internet advertising market has adopted first price auctions as the preferred paradigm for selling advertising slots. Our work, thus, takes an important step toward understanding the implications of the shift to first price auctions in internet advertising markets by studying how the choice of the selling mechanism impacts revenues, welfare, and advertisers’ bidding strategies. This paper was accepted by Itai Ashlagi, revenue management and market analytics. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.4719 .
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Ibex: Privacy-preserving Ad Conversion Tracking and Bidding
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. We think this makes Ibex capable
of preserving a good experience while improving user privacy.
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- Award ID(s):
- 2045861
- NSF-PAR ID:
- 10392348
- Date Published:
- Journal Name:
- AACM SIGSAC Conference on Computer and Communications Security
- Page Range / eLocation ID:
- 3223 to 3237
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3548606.3560651
