SMAP: A Joint Dimensionality Reduction Scheme for Secure Multi-Party Visualization

Jiazhi Xia, Tianxiang Chen, Lei Zhang, Wei Chen, Yang Chen, Xiaolong Zhang, Cong Xie, Tobias Schreck

External link (DOI)
View presentation:2020-10-30T16:00:00ZGMT-0600Change your timezone on the schedule page
2020-10-30T16:00:00Z
Exemplar figure
Figure (a) is the architecture of SMAP. The system is set on a server to organize online joint embedding tasks. The server connects and organizes the two collaborators and multiple participants. Collaborators S and T are distributed on two different sites and connected to each other. Figure (b) is the joint embedding results of health records, including the density map of all data and the partial density map of hospital A, B, and C, respectively.
Fast forward

Direct link to video on YouTube: https://youtu.be/ckm5b5slF7Y

Keywords

High-dimensional Data Visualization, Secure Visualization, Dimensionality Reduction, Secure Multi-Party Computation

Abstract

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.