Welcome to the resource topic for 2025/1891
Title:
Fraud Mitigation in Privacy-Preserving Attribution
Authors: Rutchathon Chairattana-Apirom, Stefano Tessaro, Nirvan Tyagi
Abstract:Privacy-preserving advertisement attribution allows websites selling goods to learn statistics on which advertisement campaigns can be attributed to converting sales. Existing proposals rely on users to locally store advertisement history on their browser and report attribution measurements to an aggregation service (instantiated with multiparty computation over non-colluding servers). The service computes and reveals the aggregate statistic. The service hides individual user contributions, but it does not guarantee integrity against misbehaving users that may submit fraudulent measurements.
Our work proposes a new cryptographic primitive, “secret share attestation”, in which secret shares input into a multiparty computation protocol are accompanied by an attestation of integrity by a third party: advertisers include signature attestations when serving ads that are later included in contributed measurements. We propose two constructions based on the standards-track BBS signatures and efficient signatures over equivalence classes, respectively. We implement and evaluate our protocols in the context of the advertising application to demonstrate their practicality.
ePrint: https://eprint.iacr.org/2025/1891
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