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

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.