SMAP: A Joint Dimensionality Reduction Scheme for Secure Multi-Party Visualization
This addresses privacy concerns for stakeholders in multi-party data analysis, though it is incremental as it adapts an existing method to a new infrastructure.
The paper tackles the problem of performing dimensionality reduction on distributed datasets without compromising data privacy by reformulating t-SNE into a secure multi-party scheme, resulting in a prototype system demonstrated through case studies including real-world deployment.
Nowadays, as data becomes increasingly complex and distributed, data analyses often involve several related datasets that are stored on different servers and probably owned by different stakeholders. While there is an emerging need to provide these stakeholders with a full picture of their data under a global context, conventional visual analytical methods, such as dimensionality reduction, could expose data privacy when multi-party datasets are fused into a single site to build point-level relationships. In this paper, we reformulate the conventional t-SNE method from the single-site mode into a secure distributed infrastructure. We present a secure multi-party scheme for joint t-SNE computation, which can minimize the risk of data leakage. Aggregated visualization can be optionally employed to hide disclosure of point-level relationships. We build a prototype system based on our method, SMAP, to support the organization, computation, and exploration of secure joint embedding. We demonstrate the effectiveness of our approach with three case studies, one of which is based on the deployment of our system in real-world applications.