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:过去十年来,在线广告业对方案广告给予了极大关注。实时投标系统(RTB)正在迅速成为最受欢迎的买卖网上广告印象的方法。在RTB系统中,需求方平台(DSP)的目标是有效利用广告商的竞选预算,同时最大限度地增加利润,寻找导致用户反应高的印象,如点击或安装。在目前的研究中,我们从特定DSP的角度对移动游戏应用程序安装的预测过程进行了调查,同时关注用户隐私,探索隐私保护与模型性能之间的交换。在RTB系统中,需求方平台(DSP)的目标是根据与数据共享进程相关的隐私泄露情况,例如数据转换或去匿名化,高效地花费广告商的竞选预算。为解决这些关切,提出了隐私保护技术,例如密码学方法,用于培训隐私意识机器学习模式。然而,在不使用用户级别数据数据的同时,培训移动游戏软件安装预测模型的能力,可以防止这些威胁,并保护用户隐私的交换能力,尽管每个用户的数据收集能力都可能使用户的数据收集能力受损。