从Stackelberg博弈看机器学习与数据混淆的冲突
A Stackelberg Game Perspective on the Conflict Between Machine Learning and Data Obfuscation
摘要：
数据是新的石油;这句话在互联网跟踪、机器学习和数据分析时代被广泛重复。然而，随着数据汇集变得越来越个人化和普遍化，公众要求保护隐私保护的压力也越来越大。在这种氛围下，开发人员已经创建了应用程序，以向跟踪算法可见的用户属性添加噪声。当维护隐私和提高准确性的激励措施不一致时，这就在跟踪者和用户之间建立了一种战略互动。在本文中，我们通过一个N+1-玩家，增广Stackelberg游戏概念化这种冲突。首先，机器学习器声明一个隐私保护级别，然后用户通过选择他们自己的扰动量来做出响应。我们使用差分隐私和经验风险最小化的一般框架来量化的效用组件，由于隐私和准确性，分别。在平衡中，每个用户独立地干扰她的数据，这导致准确性的高净损失。为了补救这种情况下，我们表明，学习者提高了他的效用，主动扰动自己的数据。虽然在这一领域的其他工作已经研究了隐私市场和机制设计的真实报告的用户信息，我们采取了不同的观点，考虑用户和学习者的扰动。
英文摘要：
Data is the new oil; this refrain is repeated extensively in the age of internet tracking, machine learning, and data analytics. As data collection becomes more personal and pervasive, however, public pressure is mounting for privacy protection. In this atmosphere, developers have created applications to add noise to user attributes visible to tracking algorithms. This creates a strategic interaction between trackers and users when incentives to maintain privacy and improve accuracy are misaligned. In this paper, we conceptualize this conflict through an N+1-player, augmented Stackelberg game. First a machine learner declares a privacy protection level, and then users respond by choosing their own perturbation amounts. We use the general frameworks of differential privacy and empirical risk minimization to quantify the utility components due to privacy and accuracy, respectively. In equilibrium, each user perturbs her data independently, which leads to a high net loss in accuracy. To remedy this scenario, we show that the learner improves his utility by proactively perturbing the data himself. While other work in this area has studied privacy markets and mechanism design for truthful reporting of user information, we take a different viewpoint by considering both user and learner perturbation.
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