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.

翻译：过去十年间，程序化广告在在线广告产业中受到广泛关注。实时竞价（RTB）系统正迅速成为买卖在线广告曝光量最主流的方式。在RTB系统中，需求方平台（DSP）旨在高效利用广告商的活动预算，同时最大化利润，寻找能带来高用户响应（如点击或安装）的曝光机会。本研究从特定DSP视角出发，探讨手游应用安装的预测过程，重点关注用户隐私，并探索隐私保护与模型性能之间的权衡。根据数据共享过程中（如数据转换或去匿名化）相关的隐私泄露情况，用户隐私面临多个层面的潜在威胁。为解决这些问题，研究者提出了隐私保护技术（如密码学方法），用于训练隐私感知的机器学习模型。然而，在不使用用户级数据的情况下训练手游安装预测模型的能力，能够防范这些威胁并保护用户隐私，尽管模型的预测能力可能因此受损。此外，现行法律可能要求企业声明正在收集数据，甚至允许用户选择退出此类数据收集，这可能会威胁依赖用户级数据收集与使用的数字广告企业商业模式。我们得出结论：隐私感知模型仍可能保留显著能力，使企业能够根据各案例的隐私-效能权衡效用函数做出更优决策。