Building Probabilistic and Isolated Learning models on Differentially private data for Campaign Optimisation in Programmatic setting

Abstract

As we embark on an era of data-driven decisions, preserving the privacy of the underlying data is of utmost importance. Differential privacy has emerged as the go-to solution for tech giants and data vendors as it not only provides a mathematical definition of privacy but also grants the ability to control the privacy parameter (noise) in the underlying data. The differentially private data aggregates sensitive individual data over multiple features and masks them with statistical noise. The conventional ML algorithms need to be tweaked to handle such aggregated data with noise. In this paper, probabilistic and Isolated learning techniques are leveraged to model differentially private data to improve the click-through rate of Ad-tech campaigns. The ability to predict the click event for different feature combinations such as URL, Ad type, Device type, etc., beforehand from the aggregated noisy data will help develop a custom bidding strategy in a highly competitive programmatic setting (Real-time bidding).