Data privacy and ownership are significant in social data science, raising legal and ethical concerns. Sharing and analyzing data is difficult when different parties own different parts of it. An approach to this challenge is to apply de-identification or anonymization techniques to the data before collecting it for analysis. However, this can reduce data utility and increase the risk of re-identification. To address these limitations, we present PADME, a distributed analytics tool that federates model implementation and training. PADME uses a federated approach where the model is implemented and deployed by all parties and visits each data location incrementally for training. This enables the analysis of data across locations while still allowing the model to be trained as if all data were in a single location. Training the model on data in its original location preserves data ownership. Furthermore, the results are not provided until the analysis is completed on all data locations to ensure privacy and avoid bias in the results.

翻译：数据隐私与所有权在社会数据科学中具有重要意义，引发了法律和伦理方面的关切。当不同方拥有同一数据集的不同部分时，共享和分析数据变得困难。应对这一挑战的方法是在收集分析数据前对其应用去标识化或匿名化技术。然而，这会降低数据效用并增加重识别风险。为解决这些局限性，我们提出了PADME——一种实现模型训练与部署联邦化的分布式分析工具。PADME采用联邦化方法，由所有参与方共同实现并部署模型，逐步访问每个数据位置进行增量训练。这种方法能够跨数据位置进行分析，同时使模型训练效果如同所有数据集中于单一位置。在数据原始位置训练模型可保留数据所有权。此外，只有在所有数据位置完成分析后才会输出结果，以确保隐私性并避免结果偏差。