arXiv (All)
open access
Publishing Below-Threshold Triangle Counts under Local Weight Differential Privacy
We propose an algorithm for counting below-threshold triangles in weighted graphs under local weight differential privacy. While prior work has largely focused on unweighted graphs, edge weights are intrinsic to many real-world networks. We consider the setting in which the graph topology is publicl
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy#353049
- PriDyG: Privacy-preserving Dynamic Graph Inference with LLM-GNN Collaboration#429283
- Dependency Triad: A Metric to Quantify the Dependencies Between Attributes for Local Differential Privacy#429294
- Privacy Leakage via Output Label Space and Differentially Private Continual Learning#163068
- DFB-PPGSQ: Dynamic Forward-Private and Epochal Backward-Private Graph Similarity Matching Query over Encrypted Sensor Graph Databases.#455558
Record · ID 1325073
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive