  Over the past decade, programmatic advertising has received a great deal of
attention in the online advertising industry. A real-time bidding (RTB) system
is rapidly becoming the most popular method to buy and sell online advertising
impressions. Within the RTB system, demand-side platforms (DSP) aim to spend
advertisers' campaign budgets efficiently while maximizing profit, seeking
impressions that result in high user responses, such as clicks or installs. In
the current study, we investigate the process of predicting a mobile gaming app
installation from the point of view of a particular DSP, while paying attention
to user privacy, and exploring the trade-off between privacy preservation and
model performance. There are multiple levels of potential threats to user
privacy, depending on the privacy leaks associated with the data-sharing
process, such as data transformation or de-anonymization. To address these
concerns, privacy-preserving techniques were proposed, such as cryptographic
approaches, for training privacy-aware machine-learning models. However, the
ability to train a mobile gaming app installation prediction model without
using user-level data, can prevent these threats and protect the users'
privacy, even though the model's ability to predict may be impaired.
Additionally, current laws might force companies to declare that they are
collecting data, and might even give the user the option to opt out of such
data collection, which might threaten companies' business models in digital
advertising, which are dependent on the collection and use of user-level data.
We conclude that privacy-aware models might still preserve significant
capabilities, enabling companies to make better decisions, dependent on the
privacy-efficacy trade-off utility function of each case.

Machine Learning