Use of Multiparty Computation for Measurement of Ad Performance Without Exchange of Personally Identifiable Information (PII) (International Journal of Engineering & Extended Technologies Research (IJEETR), 2020)
The digital advertising ecosystem, particularly in auction-based platforms using real-time bidding (RTB), increasingly relies on large-scale data collection for performance measurement. Traditionally, this data exchange involves the use of personally identifiable information (PII), which raises significant privacy concerns. This paper explores the potential for applying multiparty computation (MPC) to evaluate user-based randomized control trials (RCTs) for ad performance measurement in a manner that ensures privacy preservation by not exchanging PII. We investigate how MPC can facilitate collaborative analysis among multiple stakeholders in the advertising ecosystem—advertisers, demand-side platforms (DSPs), supply-side platforms (SSPs), and publishers—while maintaining user confidentiality. Additionally, the paper delves into the specific challenges, benefits, and practical applications of MPC in this context, providing insight into how privacy-preserving methods can enhance the efficacy of A/B testing and other experimental ad measurement methodologies [1][2].
Keywords: Privacy-enhancing technologies; causal inference; digital ad platforms; auction-based RTBs; data encryption; randomized control trials