A blockchain‐based privacy‐preserving advertising attribution architecture: Requirements, design, and a prototype implementation

In the era of digital marketing, advertisements have become an indispensable part. One of the central challenges is advertising attribution which explains the amount of contribution every publisher has with the conversions. However, through observation, we have found that current advertising platforms attribution, advertisers attribution, or third-party platforms attribution all have the problems of trust, data leakage, and data forgery. To fill the gap, our work's main contribution is combining blockchain with advertising attribution to propose an architecture for improving the privacy-preserving degree and amount. In the proposed architecture, publishers, and advertisers can store real-time data on a blockchain. The attribution results are credible because blockchain is decentralized, tamper-proof, and traceable. We combine privacy set intersection and zero-knowledge proof technology to increase the privacy of flowing data. In addition, we describe a preliminary prototype in which publishers, advertisers, and advertising platforms can get the corresponding attribution details. To show its effectiveness, we analyze it from different perspectives, including communication cost, attribution accuracy, and time cost. The results show that our communication cost has significantly reduced compared to the recent studies.