feat(PrivacyEngineering): add DifferentialPrivacyBudgetAllocation benchmark

[oushihsabiy]

Adds an executable differential privacy budget allocation benchmark with deterministic instances, independent feasibility verification, utility-based scoring, calibrated baseline/reference solvers, bilingual documentation, auditable privacy references, Frontier unified metadata, and PrivacyEngineering domain README files.

[github-actions]

🤖 AI Code Review (gemini-3-flash-preview)

🇬🇧 English Analysis

1. Executive Summary

• Core Purpose: This PR introduces a new benchmark task titled "Differential Privacy Budget Allocation for Business Analytics." The goal is to optimize the allocation of a finite privacy budget ($\epsilon$) across multiple analytical queries while satisfying constraints related to sensitivity, error bounds, and group fairness.
• Modified File Structure & Modifications:
  • benchmarks/PrivacyEngineering/DifferentialPrivacyBudgetAllocation/README.md & README_zh-CN.md: Provides the task overview, interface definition, scoring logic, and execution commands.
  • benchmarks/PrivacyEngineering/DifferentialPrivacyBudgetAllocation/Task.md & Task_zh-CN.md: Detailed task description including JSON schemas for input/output and objective functions.
  • benchmarks/PrivacyEngineering/DifferentialPrivacyBudgetAllocation/benchmark.yaml: Configuration file defining metadata, seeds, timeouts, and Docker runtime environment.
  • benchmarks/PrivacyEngineering/DifferentialPrivacyBudgetAllocation/baseline/: Contains heuristic.py and weak.py providing baseline implementations for comparison.
  • benchmarks/PrivacyEngineering/DifferentialPrivacyBudgetAllocation/frontier_eval/: Integration files for the evaluation framework (constraints, commands, and file lists).
  • benchmarks/PrivacyEngineering/DifferentialPrivacyBudgetAllocation/verification/: (Implied by imports) Contains problem.py and evaluator.py for instance generation and solution validation.

2. AI Content Analysis

• Estimated AI Component: 30%
• Reasoning & Evidence: The overall structure follows a highly standardized template (BenchGen). The baseline/*.py files exhibit boilerplate patterns typical of AI-assisted generation, such as the standard sys.path manipulation and if __name__ == "__main__": blocks. However, the domain-specific logic—specifically the formulation of the DP utility function ($1.0 + \sum business_value \cdot coverage \cdot \log1p(\epsilon) - penalties$) and the fairness ratio constraints—suggests significant human design or highly specialized prompting.

3. Engineering & Economic Assessment

• Engineering Reality Check: This addresses a sophisticated, production-grade problem in Privacy Engineering. Unlike "toy" DP examples that focus on a single mechanism, this task models a portfolio optimization problem. It correctly identifies real-world constraints: minimum $\epsilon$ for utility, maximum $\epsilon$ for privacy, and the $sensitivity/\epsilon$ error relationship. The inclusion of max_group_error_ratio addresses modern algorithmic fairness requirements.
• Economic Value: High. Efficient privacy budget management is a critical bottleneck for data-driven companies (e.g., finance, healthcare) operating under GDPR/CCPA. Optimizing this allocation directly enables higher data utility (revenue) for the same privacy risk (compliance cost).

4. Quality Assurance

• Verification & Testing:
  • frontier_eval Integration: Yes.
  • task_name: DifferentialPrivacyBudgetAllocation
  • Execution & Dependencies: The README.md clearly documents both local execution (python verification/evaluator.py) and Docker-based execution with a specific image hash, ensuring reproducibility.
• Documentation Quality: High. The documentation provides clear JSON schemas and mathematical definitions of the objective function. The dual-language support is consistent. No significant grammatical errors were detected in the provided diff.
• Organizational Structure: The structure is logical and modular, separating the problem definition (Task.md), the verification logic (verification/), and the baseline implementations.

5. Security & Privacy Check

• Sensitive Files: Clean. No .env, API keys, or IDE-specific configurations were found in the diff.
• Absolute Paths: None detected. The scripts use relative path resolution via Path(__file__).resolve().parents[1], which is best practice for portability.

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🇨🇳 中文分析

1. 摘要

• 核心目的: 此 PR 引入了一个名为“面向业务分析的差分隐私预算分配”的新基准测试任务。其目标是在满足敏感度、误差边界和组间公平性约束的前提下，优化有限隐私预算 ($\epsilon$) 在多个分析查询中的分配。
• 修改的文件结构与变更摘要:
  • README.md & README_zh-CN.md: 提供任务概览、接口定义、评分逻辑和执行命令。
  • Task.md & Task_zh-CN.md: 详细的任务描述，包括输入/输出的 JSON Schema 和目标函数。
  • benchmark.yaml: 配置文件，定义元数据、种子、超时时间和 Docker 运行环境。
  • baseline/: 包含 heuristic.py 和 weak.py，提供用于对比的基准实现。
  • frontier_eval/: 评测框架的集成文件（约束、命令和文件列表）。
  • verification/: （通过导入体现）包含用于实例生成和方案验证的 problem.py 和 evaluator.py。

2. AI 成分分析

• 预估 AI 含量: 30%
• 判断依据与证据: 整体结构遵循高度标准化的模板 (BenchGen)。baseline/*.py 文件表现出典型的 AI 辅助生成的样板模式，例如标准的 sys.path 处理和 if __name__ == "__main__": 代码块。然而，特定领域的逻辑——特别是差分隐私效用函数 ($1.0 + \sum business_value \cdot coverage \cdot \log1p(\epsilon) - penalties$) 的公式化以及公平性比例约束——表明了显著的人工设计或高度专业化的提示词工程。

3. 工程与经济评估

• 工程现实检验: 该任务解决了隐私工程中一个复杂的生产级问题。与关注单一机制的“玩具级”差分隐私示例不同，此任务建模了一个组合优化问题。它准确识别了现实世界的约束：保证效用的最小 $\epsilon$、保证隐私的最大 $\epsilon$ 以及 $sensitivity/\epsilon$ 的误差关系。引入 max_group_error_ratio 解决了现代算法公平性的要求。
• 经济价值: 高。对于在 GDPR/CCPA 监管下运行的数据驱动型公司（如金融、医疗），高效的隐私预算管理是关键瓶颈。优化此类分配可以直接在相同的隐私风险（合规成本）下实现更高的数据效用（收入）。

4. 质量保证

• 验证与测试:
  • frontier_eval 集成: 是
  • task_name: DifferentialPrivacyBudgetAllocation
  • 运行与依赖: README.md 清晰地记录了本地执行命令和基于特定镜像哈希的 Docker 执行命令，确保了可重复性。
• 文档质量: 高。文档提供了清晰的 JSON Schema 和目标函数的数学定义。双语支持保持了一致性。在提供的 diff 中未检测到明显的语法错误。
• 组织结构: 文件组织逻辑清晰且模块化，将问题定义 (Task.md)、验证逻辑 (verification/) 和基准实现进行了有效分离。

5. 安全与隐私检查

• 敏感文件: 未发现异常。Diff 中未发现 .env、API 密钥或 IDE 配置文件。
• 绝对路径: 未检测到。脚本使用 Path(__file__).resolve().parents[1] 进行相对路径解析，符合可移植性最佳实践。