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:在过去的十年中,程序化广告在在线广告行业中受到了极大关注。实时竞价(RTB)系统正在快速成为购买和销售在线广告展示次数的最流行方法。在RTB系统中,需求方平台(DSP)旨在有效地利用广告主的广告预算,同时最大化利润,寻找引起高用户响应的展示次数,例如点击或安装。在当前的研究中,我们从DSP的角度研究了预测移动游戏应用安装的过程,同时注意用户隐私,并探讨了隐私保护和模型性能之间的权衡。可能会对用户隐私造成多种不同级别的潜在威胁,这取决于与数据共享过程相关联的隐私泄漏,例如数据转换或去匿名化。为了解决这些问题,提出了隐私保护技术,例如用于训练隐私感知机器学习模型的密码学方法。然而,即使可能会影响模型的预测能力,训练不使用用户级数据的移动游戏应用安装预测模型,可以防止这些威胁并保护用户隐私。此外,当前的法律可能会强制公司声明他们在收集数据,并可能给用户选择退出该数据收集的选项,这可能会威胁数字广告公司的商业模式,该模式依赖于收集和使用用户级数据。我们得出结论,隐私感知模型仍然可以保留重要的能力,使公司能够做出更好的决策,这取决于每种情况的隐私效益权衡实用程序功能。