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"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": []
}
]
}
} |