Graph Theory for Consent Management: A New Approach for Complex Data Flows
ACM SIGMOD Record(2024)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要