Through legislation and technical advances users gain more control over how their data is processed, and they expect online services to respect their privacy choices and preferences. However, data may be processed for many different purposes by several layers of algorithms that create complex data workflows. To date, there is no existing approach to automatically satisfy fine-grained privacy constraints of a user in a way which optimises the service provider's gains from processing. In this article, we propose a solution to this problem by modelling a data flow as a graph. User constraints and processing purposes are pairs of vertices which need to be disconnected in this graph. In general, this problem is NP-hard, thus, we propose several heuristics and algorithms. We discuss the optimality versus efficiency of our algorithms and evaluate them using synthetically generated data. On the practical side, our algorithms can provide nearly optimal solutions for tens of constraints and graphs of thousands of nodes, in a few seconds.

中文翻译：

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同意管理的图论：复杂数据流的新方法

通过立法和技术进步，用户可以更好地控制其数据的处理方式，并且他们期望在线服务尊重他们的隐私选择和偏好。然而，数据可以通过创建复杂数据工作流的多层算法来处理用于许多不同的目的。迄今为止，还没有现有的方法能够以优化服务提供商从处理中获得的收益的方式自动满足用户的细粒度隐私约束。在本文中，我们通过将数据流建模为图来提出此问题的解决方案。用户约束和处理目的是需要在该图中断开连接的顶点对。一般来说，这个问题是NP-hard的，因此，我们提出了几种启发式方法和算法。我们讨论算法的最优性与效率，并使用综合生成的数据对其进行评估。在实践方面，我们的算法可以在几秒钟内为数十个约束和数千个节点的图表提供近乎最优的解决方案。