New Algorithms for Differentially Private Subgraph Counting in Range Queries
Researchers have introduced the first efficient algorithms for differentially private range subgraph counting (DPRSC), enabling private analysis of subgraph patterns within attribute-defined vertex subsets. The work addresses the high sensitivity of subgraph counting — where a single edge change can affect many subgraph occurrences — by using a subgraph projection technique that reduces the problem to weighted orthogonal range counting. The study also establishes matching lower bounds, proving that any differentially private DPRSC algorithm must incur additive error exponential in the query dimension.
A paper accepted to ICML 2026 introduces the first efficient algorithms for differentially private range subgraph counting (DPRSC), a problem motivated by real-world graph analytics performed on vertex-induced subgraphs rather than entire graphs. Unlike simpler point-counting queries, subgraph counting is nonlinear and highly sensitive, making differential privacy difficult to achieve with low error. The proposed approach uses a subgraph projection to reduce DPRSC to weighted orthogonal range counting, then applies range trees and local sensitivity estimation to answer queries accurately under privacy constraints. Complementing the algorithmic contributions, the authors derive matching lower bounds by reducing reconstruction attacks to DPRSC and invoking discrepancy theory, showing that exponential additive error in the dimension is unavoidable for any differentially private algorithm in this setting. Empirical evaluations on real datasets show the new algorithms substantially outperform baseline methods in both accuracy and runtime while maintaining strong privacy guarantees.
What different sources said
- arXiv cs.LGCenter
Differentially Private Range Subgraph Counting
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