New Algorithms for Differentially Private Subgraph Counting in Range Queries
Researchers have developed the first efficient algorithms for differentially private range subgraph counting (DPRSC), a problem that counts pattern occurrences in graph subsets while protecting privacy. The work addresses the challenge that subgraph counting is inherently nonlinear and sensitive to edge modifications, unlike simpler counting tasks. This advance is significant for enabling privacy-preserving graph analytics in applications where data sensitivity requires formal privacy guarantees.
A new arXiv paper introduces algorithms for differentially private range subgraph counting, tackling the problem of counting occurrences of a fixed pattern graph within induced subgraphs defined by multi-dimensional attribute ranges while maintaining differential privacy. The core challenge is that subgraph counting exhibits high sensitivity—a single edge modification can affect many subgraph occurrences—making it fundamentally different from classical point counting. The researchers' approach uses subgraph projection to reduce the problem to weighted orthogonal range counting, leveraging range trees and local sensitivity estimation to achieve accurate private query answering. The paper provides both algorithmic contributions and theoretical lower bounds, showing that any differentially private algorithm for DPRSC must incur additive error exponential in the dimension. Empirical evaluations demonstrate that the proposed algorithms significantly outperform baseline methods in both accuracy and runtime while maintaining strong privacy guarantees.
What's missing
The paper's own limitations and open questions are not detailed in the abstract provided. Specific details about the empirical evaluation (datasets used, baseline comparisons, privacy parameter settings) are not included in the abstract.
What different sources said
- arXiv cs.LGCenter
Differentially Private Range Subgraph Counting
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