Privacy is an essential issue in data trading markets. This work uses a mechanism design approach to study the optimal market model to economize the value of privacy of personal data, using differential privacy. The buyer uses a finite number of randomized algorithms to get access to the owners' data in a sequential-composition manner, in which each randomized algorithm is differentially private. Each usage of a randomized algorithm is referred to as a period. Due to the composability of differential privacy, there are inevitable privacy losses accumulated over periods. Hence, we allow the owners to leave the market at the end of any period by making stopping decisions. We define an instrumental kernel function to capture the instrumentalness of owners' preferences and model the formation of each owner's (both intrinsic and instrumental) preference over periods by taking into consideration the composability of differential privacy and the time-varying nature of privacy concerns. Our desideratum is to study the buyer's design regime of optimal market models in a dynamic environment when each owner makes coupled decisions of stopping and reporting of their preferences. The buyer seeks to design a privacy allocation rule that dynamically specifies the degree of privacy protections and a payment rule to compensate for the privacy losses of the owners. The buyer additionally chooses a payment rule which is independent of owners' reports of their preferences to influence the owners' stopping decisions. We characterize the dynamic incentive compatibility and provide a design principle to construct the payment rules in terms of the privacy allocation rule. Further, we relax the buyer's market design problem and provide a sufficient condition for an approximated dynamic incentive-compatible market model.
翻译:隐私是数据交易市场的一个基本问题。 这项工作使用一种机制设计方法, 研究最佳市场模式, 以节省个人数据隐私的价值, 使用不同的隐私。 买方使用数量有限的随机算法, 以顺序组合方式获取所有者的数据, 每种随机算法都是不同的私人。 随机算法的每种使用都被称为一个时期。 由于不同隐私的兼容性, 隐私损失必然会累积一段时间。 因此, 我们允许所有者在任何时期结束时通过停止决定而退出市场。 我们定义了一种工具内核功能, 以捕捉所有者偏好的工具性, 并模型显示每个所有者( 内在的和工具的)优待时间的形成, 同时考虑到不同隐私的可兼容性以及隐私关切的时间变化性质。 我们的套头将研究买方在动态环境中设计最佳市场模式的设计机制, 当每个所有者同时做出停止和报告其偏好选择的动机时。 买主试图设计一个隐私分配规则, 动态分配规则将明确每个所有者( ) 的保密性规则的准确性, 以及支付规则的准确性规则将使得购买者在市场设计中具有充分的支付规则的准确性。 我们的准确性规则可以补偿。