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Aggregate grading is crucial in economically obtaining a concrete mix with adequate strength, reasonable workability, and minimal segregation. There are four types of aggregate gradation: well-graded, uniformly (or one-sized) graded, gap-graded, and open-graded.
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Locally Differentially Private Heterogeneous Graph Aggregation with Utility Optimization.

Zichun Liu1, Liusheng Huang1, Hongli Xu1

  • 1School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China.

Entropy (Basel, Switzerland)
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Summary

This study introduces new methods for privacy-preserving graph data aggregation using attributewise local differential privacy (ALDP). These techniques offer stronger protection for heterogeneous graphs than existing single-edge privacy solutions.

Keywords:
data privacygraph aggregationheterogeneous graphlocal differential privacy

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Area of Science:

  • Computer Science
  • Data Privacy
  • Graph Theory

Background:

  • Graph data is essential for various organizational functions, but data breaches pose significant risks.
  • Existing local differential privacy (LDP) methods for graph aggregation primarily focus on single-edge privacy, leaving complex graph structures vulnerable.
  • There is a critical need for robust privacy mechanisms that can handle heterogeneous and attributed graph data.

Purpose of the Study:

  • To develop advanced local differential privacy (LDP) mechanisms for aggregating mixed attributed graph data.
  • To enhance privacy protection beyond single-edge privacy for complex graph structures.
  • To maintain data utility and intrinsic associations while ensuring strong privacy guarantees.

Main Methods:

  • Introduction of attributewise local differential privacy (ALDP) for moderate granularity privacy.
  • Formulation of graph data aggregation as collecting statistics under ALDP.
  • Development of the PrivAG mechanism for categorical-attributed graphs by randomizing attribute and degree vectors.
  • Proposal of an adaptive binning scheme (ABS) for heterogeneous graphs, leading to the generalized PrivHG mechanism.

Main Results:

  • The proposed PrivAG and PrivHG mechanisms effectively aggregate mixed attributed graph data with enhanced privacy.
  • ALDP provides stronger privacy protection compared to traditional single-edge LDP methods.
  • Experimental validation demonstrates superior effectiveness and efficiency over state-of-the-art mechanisms.
  • Optimized utility through reduced computation costs and estimation errors.

Conclusions:

  • The developed ALDP-based mechanisms offer a significant advancement in privacy-preserving graph data aggregation.
  • These methods successfully address the challenge of aggregating heterogeneous attributed graph data with strong privacy guarantees.
  • The research provides practical and efficient solutions for organizations handling sensitive graph-structured information.