| [ | |
| "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).", | |
| "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).", | |
| "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).", | |
| "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).", | |
| "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).", | |
| "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)." | |
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