论文标题
联合可视化：汇总视觉查询的隐私策略
Federated Visualization: A Privacy-preserving Strategy for Aggregated Visual Query
论文作者
论文摘要
我们提出了一种新颖的隐私保护策略，用于分散视觉化。关键思想是模仿联合学习框架的流程图，并在联合基础架构中重新重新进行可视化过程。通过利用一个共享的全局模块来实现可视化联合，该模块组成了本地模块中数据片的转换视觉特征的加密外部化。我们设计了联合可视化的两个实现：基于预测的方案和一个基于查询的方案。我们通过一组视觉形式证明了方法的有效性，并通过评估来验证其鲁棒性。我们通过专家审查报告了在实际场景中联合可视化的价值。
We present a novel privacy preservation strategy for decentralized visualization. The key idea is to imitate the flowchart of the federated learning framework, and reformulate the visualization process within a federated infrastructure. The federation of visualization is fulfilled by leveraging a shared global module that composes the encrypted externalizations of transformed visual features of data pieces in local modules. We design two implementations of federated visualization: a prediction-based scheme, and a query-based scheme. We demonstrate the effectiveness of our approach with a set of visual forms, and verify its robustness with evaluations. We report the value of federated visualization in real scenarios with an expert review.
