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：过去十年中，程序化广告在在线广告行业备受关注。实时竞价系统正迅速成为购买和出售在线广告曝光的最主流方式。在该系统中，需求方平台致力于高效分配广告商的活动预算以最大化利润，并寻求能产生高用户响应（例如点击或安装）的广告曝光。本研究从特定需求方平台的角度出发，探讨移动游戏应用安装的预测过程，同时关注用户隐私问题，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）可能引发的隐私泄露程度，用户隐私面临多层次的潜在威胁。为应对这些挑战，研究人员提出了隐私保护技术，例如基于密码学的方法来训练隐私感知机器学习模型。然而，在不使用用户级数据的情况下训练移动游戏安装预测模型的能力，虽可能削弱模型的预测性能，却能有效防范上述威胁并保护用户隐私。此外，现行法律可能要求企业声明其数据收集行为，甚至赋予用户选择退出此类数据收集的权利，这或将威胁依赖用户级数据收集与利用的数字广告商业模式。我们得出结论：隐私感知模型仍可能保留显著的能力，使企业能够根据各案例的隐私-效用权衡函数做出更优决策。