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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE Conference on Visual Analytics Science and Technology, VAST 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages107-118
Number of pages12
ISBN (Electronic)9781728180090
DOIs
StatePublished - Oct 2020
Event15th IEEE Conference on Visual Analytics Science and Technology, VAST 2020 - Virtual, Salt Lake City, United States
Duration: Oct 25 2020Oct 30 2020

Publication series

NameProceedings - 2020 IEEE Conference on Visual Analytics Science and Technology, VAST 2020

Conference

Conference15th IEEE Conference on Visual Analytics Science and Technology, VAST 2020
Country/TerritoryUnited States
CityVirtual, Salt Lake City
Period10/25/2010/30/20

All Science Journal Classification (ASJC) codes

  • Media Technology
  • Modeling and Simulation

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