Paper 2022/1174

Ibex: Privacy-preserving ad conversion tracking and bidding (full version)

Ke Zhong, University of Pennsylvania
Yiping Ma, University of Pennsylvania
Sebastian Angel, University of Pennsylvania, Microsoft Research (United States)

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.

Note: This version includes appendices. The latest changes discuss the leakage from the noise of the RLWE cryptosystem and how to address it.

Available format(s)
Publication info
Published elsewhere. ACM CCS 2022
Online advertising privacyPrivate aggregationOblivious bidding
Contact author(s)
kezhong @ seas upenn edu
yipingma @ seas upenn edu
sebastian angel @ cis upenn edu
2023-04-02: revised
2022-09-08: received
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Creative Commons Attribution


      author = {Ke Zhong and Yiping Ma and Sebastian Angel},
      title = {Ibex: Privacy-preserving ad conversion tracking and bidding (full version)},
      howpublished = {Cryptology ePrint Archive, Paper 2022/1174},
      year = {2022},
      doi = {10.1145/3548606.3560651},
      note = {\url{}},
      url = {}
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