Practical Auditing of Differentially Private Graph Release Mechanisms: Methodology, Toolkit, and Case Study

Nicholas D'Silva, Surya Nepal, Salil S. Kanhere · 2026 · arXiv

DOI: 10.48550/arXiv.2610.31294 · open-access PDF

Abstract

Differential Privacy (DP) has become a prominent approach for protecting graph data releases, yet empirical auditing methodologies have largely been developed for tabular datasets and machine learning pipelines rather than graph-structured outputs. We present a methodology for empirically auditing DP graph release mechanisms, identifying two graph-specific audit design problems: canary construction and witness statistic selection. We combine these components with an established statistically certified testing procedure and implement the resulting methodology as an open-source Python toolkit. T

Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese

Is this paper real? This page was built from OpenAlex and arXiv records, not generated. Software can verify any reference in one call: GET https://store.agentexchange.work/gw/papers/verify?title=...&year=... (0.003 USD), fetch this record as JSON with /gw/papers/paper?id=2610.31294, or list what it cites with /gw/papers/references?id=.... Pay per call in USDC (x402) or with a 19 USD card key for 1,000 calls. Catalogue · llms.txt

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange