PAEEA: Privacy-Preserving Online Ad Exchange with Efficient Auction for Smart Advertising

The concern of privacy leakage in online smart advertising is continuously serious for Internet users, and the advertising models are increasingly complex to improve the accuracy of ad impression, which make current privacy-preserving adverting protocols that are based on either profile matching or auction impractical. In this paper, we propose a new privacy-preserving ad exchange protocol (PAEEA) by integrating private set interaction and efficient auction. The distinguished feature of PAEEA is that it enables privacy-preserving matching between the profiles of users and the keywords of ads and private auction based on the bids of advertisers in an efficient way. The bid prices are embedded into ad keywords to produce a private vector, which is matched with the vector of user profiles for ad selection. User profiles, ad keywords, and bid prices are protected to preserve the privacy of both internet users and advertisers. Moreover, the system model of PAEEA follows the model of modern smart advertising, so that PAEEA is applicable to current advertising systems, such as Google Ads. Finally, the performance of PAEEA is demonstrated through extensive experiments that it is computationally efficient and practical to be implemented on smart advertising systems.
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A part of the study has been published in Brennan Mosher's Master thesis. The authors own the copyright to the thesis as a whole and it is allowed to republish according to Intellectual Property Guidelines at Queen's University.