{ "schema_version": 1, "title": "Repro: Accuracy-First Rényi Differential Privacy and Post-Processing Immunity", "emoji": "🔐", "space_id": "snaykey/repro-one-param-subgroups", "paper": { "openreview_id": "CVDEc0doW8" }, "tags": [ "icml2026-repro", "paper-CVDEc0doW8" ], "updated_at": "2026-07-30T16:00:00+00:00", "root": { "slug": "index", "title": "Reproduction: Accuracy-First Rényi Differential Privacy and Post-Processing Immunity", "file": "pages/index.md", "children": [ { "slug": "claim-1", "title": "The paper reformulates mechanisms as returning pairs (y, ε) rather than separate output and privacy-loss functions, resolving prior notational obstacles to defining post-processing immunity for accuracy-first privacy (Section 3.1).", "file": "pages/claim-1/page.md", "children": [] }, { "slug": "claim-2", "title": "Pure ex-post privacy (δ=0) satisfies post-processing immunity, but δ-probabilistic ex-post privacy with δ>0 does not, even though the latter is shown equivalent to (ε,δ)-probabilistic differential privacy for constant ε (Theorem 3.2, Section 3.2).", "file": "pages/claim-2/page.md", "children": [] }, { "slug": "claim-3", "title": "The paper introduces α-ex-post Rényi differential privacy (Section 4), proves it satisfies post-processing immunity (Theorem 4), and proves it composes adaptively with total privacy loss ε* = Σε_i (Theorem 6).", "file": "pages/claim-3/page.md", "children": [] }, { "slug": "claim-4", "title": "The sequential precision-weighted Gaussian mechanism (Algorithm 2, Appendix C.4) is shown to achieve α-ex-post RDP equivalent to the original Brownian mechanism (Theorem 5, Section 5).", "file": "pages/claim-4/page.md", "children": [] }, { "slug": "claim-5", "title": "Table 1 summarizes that pure ex-post privacy has post-processing immunity but the Brownian mechanism does not satisfy it, whereas α-ex-post RDP satisfies both post-processing immunity and compatibility with the Brownian mechanism (Table 1, Section 3).", "file": "pages/claim-5/page.md", "children": [] }, { "slug": "claim-6", "title": "On the Adult dataset, a data-dependent stopping rule (Algorithm 1) using a private validation set generates synthetic data while minimizing privacy budget over ε ∈ [0.01, 1] subject to maintaining classifier accuracy thresholds (Figure 1, Section 6).", "file": "pages/claim-6/page.md", "children": [] }, { "slug": "conclusion", "title": "Conclusion", "file": "pages/conclusion/page.md", "children": [] } ] } }