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Graphs of functions provide a visual representation of how output values change in response to varying inputs. Each point on the graph corresponds to an ordered pair, where the x-coordinate (independent variable) determines the horizontal position and the y-coordinate (dependent variable) determines the vertical position. Linear functions like y = x give a straight line, indicating a constant rate of change.Nonlinear functions display more complex behaviors. Even power functions generate...
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Fast and Secure Multiparty Querying over Federated Graph Databases.

Nouf Aljuaid1,2, Alexei Lisitsa2, Sven Schewe2

  • 1Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif, 21944 Saudi Arabia.

SN Computer Science
|December 1, 2025
PubMed
Summary
This summary is machine-generated.

We introduce a privacy-preserving multi-party querying (PPMQ) framework for federated graph databases. PPMQ offers efficient and secure data analysis using Secure Multi-Party Computation (SMPC), outperforming existing solutions.

Keywords:
Federated databasesGraph databasesNeo4jSMPCSecure multi-party querying

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Database Systems

Background:

  • Federated graph databases present unique challenges for privacy-preserving data analysis.
  • Existing solutions for multi-party querying often compromise on efficiency or security.

Purpose of the Study:

  • To develop an efficient framework for privacy-preserving multi-party querying (PPMQ) over federated graph databases.
  • To enhance data security through Secure Multi-Party Computation (SMPC) protocols.

Main Methods:

  • Developed a PPMQ framework with two distinct security protocols: client-based and server-based.
  • The server-based protocol integrates encrypted hashing for augmented security.
  • Employed an honest but curious security model.

Main Results:

  • PPMQ demonstrates execution times and overheads comparable to Neo4j Fabric.
  • PPMQ significantly outperforms previous systems like SMPQ and Conclave in efficiency.
  • The enhanced server protocol offers improved robustness against brute force attacks.

Conclusions:

  • PPMQ provides a superior solution for privacy-preserving multi-party querying in federated graph databases.
  • The framework achieves a strong balance between computational efficiency and robust data privacy.
  • PPMQ enhances security guarantees beyond existing methods.