论文标题
您不得计算我的数据：使用MPC的分布式市场的隐私数据市场的访问策略和实施
YOU SHALL NOT COMPUTE on my Data: Access Policies for Privacy-Preserving Data Marketplaces and an Implementation for a Distributed Market using MPC
论文作者
论文摘要
个人数据是各种研究和业务领域的洞察力来源。尽管我们的数据非常有价值，但它通常对隐私敏感。因此，诸如GDPR之类的法规限制了可以合法发布的数据，以及买家可以通过此敏感数据做些什么。尽管必须保护个人数据，但我们仍然可以从不会损害我们隐私的数据中出售一些见解。数据市场是一个平台，可帮助用户在协助购买者发现相关数据集的同时出售其数据。这种市场面孔的主要挑战是在提供宝贵的见解之间在保留隐私要求的同时平衡。私人数据市场试图通过在个人数据上提供隐私保护计算来解决这一挑战。这样的计算允许在个人数据上计算统计或培训机器学习模型，而无需访问数据。但是，出售数据的用户不能限制谁可以购买或允许数据类型的计算。 我们通过为私人数据市场提出灵活的访问控制体系结构来缩小后一个差距，该架构可以应用于现有数据市场。我们的体系结构使数据销售商能够定义限制可以购买数据的详细政策。此外，卖方可以控制特定买家可以在数据上购买的计算，并对其参数施加限制，以减轻隐私漏洞。然后，数据市场的计算系统在启动计算之前执行政策。 为了证明我们的方法的可行性，我们为使用MPC的分布式数据市场为Kraken Marketplace提供了实施。我们证明我们的方法是实用的，因为它引入了可忽略不计的开销，并且对几个对手来说是安全的。
Personal data is an attractive source of insights for a diverse field of research and business. While our data is highly valuable, it is often privacy-sensitive. Thus, regulations like the GDPR restrict what data can be legally published, and what a buyer may do with this sensitive data. While personal data must be protected, we can still sell some insights gathered from our data that do not hurt our privacy. A data marketplace is a platform that helps users to sell their data while assisting buyers in discovering relevant datasets. The major challenge such a marketplace faces is balancing between offering valuable insights into data while preserving privacy requirements. Private data marketplaces try to solve this challenge by offering privacy-preserving computations on personal data. Such computations allow for calculating statistics or training machine learning models on personal data without accessing the data in plain. However, the user selling the data cannot restrict who can buy or what type of computation the data is allowed. We close the latter gap by proposing a flexible access control architecture for private data marketplaces, which can be applied to existing data markets. Our architecture enables data sellers to define detailed policies restricting who can buy their data. Furthermore, a seller can control what computation a specific buyer can purchase on the data, and make constraints on its parameters to mitigate privacy breaches. The data market's computation system then enforces the policies before initiating a computation. To demonstrate the feasibility of our approach, we provide an implementation for the KRAKEN marketplace, a distributed data market using MPC. We show that our approach is practical since it introduces a negligible performance overhead and is secure against several adversaries.
