A Protocol for Privately Reporting Ad Impressions at Scale

By Matthew D. Green, Watson Ladd, Ian Miers. 2016

78 citations recorded by the source corpus.

We present a protocol to enable privacy preserving advertising reporting at scale. Unlike previous systems, our work scales to millions of users and tens of thousands of distinct ads. Our approach builds on the homomorphic encryption approach proposed by Adnostic, but uses new cryptographic proof techniques to efficiently report billions of ad impressions a day using an additively homomorphic voting schemes. Most importantly, our protocol scales without imposing high loads on trusted third parties. Finally, we investigate a cost effective method to privately deliver ads with computational private information retrieval.

Subfields: Artificial Intelligence

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Citations in the corpus

Private Stateful Information Retrieval (2018, neutral): “3243821 advertisement [25] and media consumption [27].”
PIR with Compressed Queries and Amortized Query Processing (2018, background): “Examples include: anonymous and unobservable communication [7, 54, 60], privacy-preserving media streaming [4, 44], ad delivery [42], friend discovery [14], and notifications [26].”
Communication-Computation Trade-offs in PIR (2019, background): “While the feasibility of this primitive has been resolved for a long time [16], the search for concretely efficient constructions for practical applications has been an active area of research [5, 6, 21, 26, 27, 32, 38, 50, 68].”