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Related Experiment Video

Updated: Jul 8, 2025

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
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TEE-Graph: efficient privacy and ownership protection for cloud-based graph spectral analysis.

A K M Mubashwir Alam1, Keke Chen1

  • 1TAIC Lab, Computer Science, Marquette University, Milwaukee, WI, United States.

Frontiers in Big Data
|December 18, 2023
PubMed
Summary

This study introduces TEE-Graph, a novel solution for secure graph spectral analysis in the cloud. TEE-Graph enhances data privacy and ownership protection for contributors and owners, outperforming traditional methods.

Keywords:
SGXTEEaccess patternbig graphgraph analyticsownership protection

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Area of Science:

  • Computer Science
  • Cloud Computing
  • Data Privacy

Background:

  • Big graph data, such as social networks, requires substantial computational resources.
  • Public clouds offer scalability but pose challenges for data privacy and ownership.
  • Existing solutions inadequately protect data contributors and owners.

Purpose of the Study:

  • To propose a novel Trusted Execution Environment (TEE) based solution, TEE-Graph, for confidential graph spectral analysis.
  • To address the privacy and ownership protection needs of data contributors and owners in cloud environments.
  • To develop efficient and secure methods for analyzing outsourced big graphs.

Main Methods:

  • Utilizing Trusted Execution Environment (TEE) features for confidential computing.
  • Implementing differential privacy for access-pattern protection.
  • Employing TEE-based Lanczos and Nystrom methods for efficient graph spectral analysis.

Main Results:

  • TEE-Graph demonstrates significantly lower computation, storage, and communication costs (10^3-10^5 times) compared to software crypto approaches like PrivateGraph.
  • Access-pattern protection adds only 10%-25% to the overall computation cost.
  • The approach is immune to access-pattern-based attacks.

Conclusions:

  • TEE-Graph offers a highly efficient and secure solution for outsourced graph spectral analysis.
  • It effectively addresses unique ownership and access-pattern protection issues in TEE-related graph analytics.
  • The TEE-Graph framework is extensible to other graph analytics problems requiring robust privacy and ownership guarantees.