Nicholas D'Silva, Surya Nepal, Salil S. Kanhere · 2026 · arXiv
DOI: 10.48550/arXiv.2610.31294 · open-access PDF
Differential Privacy (DP) telah menjadi pendekatan terkemuka untuk melindungi rilis data graf, namun metodologi audit empiris sebagian besar telah dikembangkan untuk dataset tabel dan pipa pembelajaran mesin daripada output yang terstruktur graf. Kami menyajikan metodologi untuk mengaudit mekanisme rilis graf DP secara empiris, mengidentifikasi dua masalah desain audit spesifik graf: konstruksi kanary dan pemilihan statistik saksi. Kami menggabungkan komponen ini dengan prosedur pengujian yang disahkan secara statistik dan menerapkan metodologi yang dihasilkan sebagai toolkit Python open-source.
Machine translation (Meta M2M100); the English original follows.
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
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