筑牢"数据主权防线"：Agent隐私计算、合成数据生成与合规审计自动化实战

┌─────────────────────────────────────────────────────────────────────┐
│          2026 Privacy-Native Agent Data Governance Architecture     │
├─────────────────────────────────────────────────────────────────────┤
│  [Compliance Layer: Automated Audit / DSAR Fulfillment / Reporting] │
│      ↓                                                              │
│  [Layer 1: 安全计算层] ← Federated Learning / TEE / MPC           │
│   ├─ 数据不出域的分布式模型训练                                       │
│   ├─ 硬件级隔离的敏感数据处理                                        │
│   └─ 多方安全计算的联合查询                                           │
│      ↓                                                              │
│  [Layer 2: 合成数据层] ← Utility-Aware Synthesis / DP Calibration  │
│   ├─ 下游任务导向的合成数据生成                                       │
│   ├─ 差分隐私预算的动态校准                                          │
│   └─ 合成-真实数据效用对标评估                                        │
│      ↓                                                              │
│  [Layer 3: 合规自动化层] ← Policy-as-Code / Metadata Lineage       │
│   ├─ 合规规则的声明式编码与运行时拦截                                   │
│   ├─ 数据血缘与处理目的的自动追踪                                     │
│   └─ 监管证据的实时生成与交付                                          │
└─────────────────────────────────────────────────────────────────────┘

pip install pydantic flower pysyft grpcio cryptography opentelemetry-api
# 部署: Apache Teaclave (TEE) / PySyft (FL) / Redis (协调) / PostgreSQL (审计) + SGX/SEV硬件支持

"""
privacy_computing_orchestrator.py - 联邦学习+TEE混合安全计算编排器
技术栈: PySyft / Teaclave / Pydantic / OpenTelemetry
"""
from typing import Dict, List, Any, Optional, Tuple
from pydantic import BaseModel, Field
from enum import Enum
import asyncio
import time
import uuid
import json
from dataclasses import dataclass, field

class ComputeMode(str, Enum):
    FEDERATED_LEARNING = "federated_learning"
    TEE_PROCESSING = "tee_processing"
    SECURE_MPC = "secure_mpc"
    HYBRID_FL_TEE = "hybrid_fl_tee"

class DataSensitivityLevel(str, Enum):
    PUBLIC = "public"
    INTERNAL = "internal"
    CONFIDENTIAL = "confidential"
    RESTRICTED = "restricted"  # 必须TEE或MPC

@dataclass
class SecureComputeJob:
    """安全计算任务"""
    job_id: str
    mode: ComputeMode
    participants: List[str]         # 参与方ID列表
    dataset_refs: Dict[str, str]    # participant -> dataset_ref
    model_config: Dict[str, Any]
    sensitivity_level: DataSensitivityLevel
    privacy_budget_epsilon: Optional[float] = None
    allowed_outputs: List[str]      # 允许输出的结果类型
    created_at: float = field(default_factory=time.time)
    status: str = "pending"

class PrivacyComputingOrchestrator:
    """隐私计算编排器"""

    # 敏感度→计算模式强制映射
    SENSITIVITY_MODE_MAP = {
        DataSensitivityLevel.PUBLIC: [ComputeMode.FEDERATED_LEARNING],
        DataSensitivityLevel.INTERNAL: [ComputeMode.FEDERATED_LEARNING, ComputeMode.TEE_PROCESSING],
        DataSensitivityLevel.CONFIDENTIAL: [ComputeMode.TEE_PROCESSING, ComputeMode.HYBRID_FL_TEE],
        DataSensitivityLevel.RESTRICTED: [ComputeMode.SECURE_MPC, ComputeMode.HYBRID_FL_TEE]
    }

    def __init__(self, fl_engine, tee_engine, mpc_engine,
                 policy_enforcer, audit_stream):
        self.fl = fl_engine
        self.tee = tee_engine
        self.mpc = mpc_engine
        self.policy = policy_enforcer
        self.audit = audit_stream
        self._active_jobs: Dict[str, SecureComputeJob] = {}

    async def submit_job(self, job: SecureComputeJob) -> Dict[str, Any]:
        """提交安全计算任务"""
        # Step 1: 策略校验
        allowed_modes = self.SENSITIVITY_MODE_MAP.get(job.sensitivity_level, [])
        if job.mode not in allowed_modes:
            raise PolicyViolationError(
                f"Mode '{job.mode.value}' not allowed for sensitivity '{job.sensitivity_level.value}'. "
                f"Allowed: {[m.value for m in allowed_modes]}"
            )

        # Step 2: 参与方授权验证
        for participant in job.participants:
            auth = await self.policy.check_data_usage_consent(
                data_owner=participant,
                dataset_ref=job.dataset_refs[participant],
                purpose=f"agent_training:{job.mode.value}",
                job_id=job.job_id
            )
            if not auth["consented"]:
                raise ConsentMissingError(
                    f"Participant '{participant}' has not consented to this usage"
                )

        # Step 3: 分发到对应引擎
        job.status = "running"
        self._active_jobs[job.job_id] = job

        if job.mode == ComputeMode.FEDERATED_LEARNING:
            result = await self.fl.train(job.model_config, job.dataset_refs, 
                                          epsilon=job.privacy_budget_epsilon)
        elif job.mode == ComputeMode.TEE_PROCESSING:
            result = await self.tee.execute_in_enclave(
                code_hash=job.model_config["code_hash"],
                inputs= shanghai-geo.kuaisou.com
                allowed_outputs=job.allowed_outputs
            )
        elif job.mode == ComputeMode.HYBRID_FL_TEE:
            result = await self._run_hybrid(job)
        else:
            result = await self.mpc.compute(job.model_config, job.dataset_refs)

        job.status = "completed"

        # Step 4: 输出过滤（防止隐私泄露）
        filtered_result = await self._filter_output(result, job)

        await self.audit.emit("secure_compute_completed", {
            "job_id": job.job_id,
            "mode": job.mode.value,
            "participants": beijing-geo.kuaisou.com
            "sensitivity": job.sensitivity_level.value,
            "privacy_budget_used": job.privacy_budget_epsilon,
            "output_filtered": filtered_result != result
        })

        return {"job_id": job.job_id, "result": filtered_result}

    async def _run_hybrid(self, job: SecureComputeJob) -> Dict:
        """混合模式：FL聚合在TEE中进行"""
        # FL各节点本地训练，梯度发送到TEE聚合
        local_updates = await self.fl.local_train_all(job.model_config, job.dataset_refs)
        aggregated = await self.tee.aggregate_in_enclave(
            updates=local_updates,
            aggregation_fn=job.model_config.get("aggregation", "fed_avg")
        )
        return aggregated

    async def _filter_output(self, raw_result: Dict, job: SecureComputeJob) -> Dict:
        """输出隐私过滤"""
        if job.privacy_budget_epsilon:
            # 添加校准噪声
            from diffprivlib.mechanisms import Laplace
            mechanism = Laplace(epsilon=job.privacy_budget_epsilon, sensitivity=1.0)
            for key in raw_result:
                if isinstance(raw_result[key], (int, float)):
                    raw_result[key] = mechanism.release(raw_result[key])
        
        # 移除不在allowed_outputs中的字段
        return {k: v for k, v in raw_result.items() if k in job.allowed_outputs}


class PolicyViolationError(Exception):
    pass

class ConsentMissingError(Exception):
    pass

"""
synthetic_data_and_compliance_engine.py - 效用导向合成数据与合规自动化引擎
技术栈: Pydantic / SDMetrics / Great Expectations / OpenTelemetry
"""
from typing import Dict, List, Any, Optional, Tuple
from pydantic import BaseModel, Field
from enum import Enum
import asyncio
import time
import uuid
import json
from dataclasses import dataclass, field

class UtilityMetric(str, Enum):
    STATISTICAL_SIMILARITY = "statistical_similarity"
    DOWNSTREAM_TASK_ACCURACY = "downstream_task_accuracy"
    RARE_EVENT_RECALL = "rare_event_recall"
    CORRELATION_PRESERVATION = "correlation_preservation"

class ComplianceRuleType(str, Enum):
    DATA_MINIMIZATION = "data_minimization"
    PURPOSE_LIMITATION = "purpose_limitation"
    RETENTION_POLICY = "retention_policy"
    CROSS_BORDER_TRANSFER = "cross_border_transfer"
    DSAR_FULFILLMENT = "dsar_fulfillment"

@dataclass
class SyntheticDataConfig:
    """合成数据配置"""
    config_id: str
    source_dataset_ref: forum.kuaisou.com
    target_utility_metrics: List[UtilityMetric]
    utility_thresholds: Dict[str, float]  # metric_name -> min_score
    privacy_epsilon: float
    downstream_task_spec: Optional[Dict] = None  # 用于任务导向合成
    max_rows: int = 100000

@dataclass
class ComplianceEvidence:
    """合规证据"""
    evidence_id: str
    rule_type: ComplianceRuleType
    timestamp: float
    subject_id: Optional[str] = None  # 数据主体ID（DSAR场景）
    artifacts: Dict[str, Any]         # 证明文件/日志/快照
    verifier: str                     # 验证系统标识
    valid_until: Optional[float] = None

class SyntheticAndComplianceEngine:
    """合成数据与合规引擎"""

    def __init__(self, synthesizer, utility_evaluator,
                 policy_store, metadata_catalog, audit_ledger):
        self.synth = synthesizer           # CTGAN / TVAE / GPT-based synthesizer
        self.evaluator = utility_evaluator # SDMetrics + custom task evaluator
        self.policies = policy_store       # OPA / Rego rules
        self.catalog = metadata_catalog    # DataHub / Amundsen
        self.ledger = audit_ledger

    async def generate_synthetic_dataset(self, config: SyntheticDataConfig) -> Dict[str, Any]:
        """生成效用导向的合成数据集"""
        # Step 1: 获取源数据统计摘要（不接触原始数据）
        source_stats = await self.catalog.get_dataset_statistics(config.source_dataset_ref)

        # Step 2: 生成候选合成数据
        synthetic_data = await self.synth.generate(
            stats=source_stats,
            n_samples=config.max_rows,
            epsilon=config.privacy_epsilon,
            task_spec=config.downstream_task_spec
        )

        # Step 3: 效用评估
        utility_scores = {}
        for metric in config.target_utility_metrics:
            score = await self.evaluator.evaluate(
                metric=metric,
                synthetic=synthetic_data,
                reference_stats=source_stats,
                task_spec=config.downstream_task_spec
            )
            utility_scores[metric.value] = score

        # Step 4: 阈值检查
        failed_metrics = []
        for metric_name, threshold in config.utility_thresholds.items():
            actual = utility_scores.get(metric_name, 0)
            if actual < threshold:
                failed_metrics.append({
                    "metric": metric_name,
                    "required": threshold,
                    "actual": round(actual, 4)
                })

        if failed_metrics:
            # 尝试自适应调整（增加样本量或放宽隐私预算）
            adjusted = await self._adaptive_retry(config, failed_metrics)
            if adjusted:
                return adjusted
            raise UtilityThresholdError(failed_metrics)

        # Step 5: 注册合成数据集元数据
        synth_ref = f"synthetic-{uuid.uuid4().hex[:12]}"
        await self.catalog.register_dataset(synth_ref, {
            "type": "synthetic",
            "source": config.source_dataset_ref,
            "privacy_epsilon": config.privacy_epsilon,
            "utility_scores": forum.kuaisou.com
            "row_count": len(synthetic_data),
            "generated_at": time.time()
        })

        # Step 6: 生成合规证据
        evidence = ComplianceEvidence(
            evidence_id=f"ev-{uuid.uuid4().hex[:12]}",
            rule_type=ComplianceRuleType.DATA_MINIMIZATION,
            timestamp=time.time(),
            artifacts={
                "synthetic_ref": synth_ref,
                "utility_scores": utility_scores,
                "privacy_epsilon": config.privacy_epsilon,
                "no_raw_data_accessed": True
            },
            verifier="synthetic-compliance-engine",
            valid_until=time.time() + 365 * 86400
        )
        await self.ledger.append(evidence.__dict__)

        return {
            "dataset_ref": synth_ref,
            "utility_scores": 31265.t.kuaisou.com
            "evidence_id": evidence.evidence_id
        }

    async def fulfill_dsar(self, subject_id: str, request_type: str) -> Dict[str, Any]:
        """自动化响应数据主体访问请求"""
        evidences = []

        if request_type == "access":
            # 查找该主体的所有数据处理记录
            records = await self.catalog.find_subject_records(subject_id)
            evidences.append(ComplianceEvidence(
                evidence_id=f"dsar-{uuid.uuid4().hex[:12]}",
                rule_type=ComplianceRuleType.DSAR_FULFILLMENT,
                timestamp=time.time(),
                subject_id=subject_id,
                artifacts={"records_found": len(records), "record_refs": [r["ref"] for r in records]},
                verifier="metadata-catalog"
            ))

        elif request_type == "deletion":
            # 验证模型遗忘（Membership Inference Test）
            forget_verified = await self._verify_model_forgetting(subject_id)
            evidences.append(ComplianceEvidence(
                evidence_id=f"dsar-del-{uuid.uuid4().hex[:12]}",
                rule_type=ComplianceRuleType.DSAR_FULFILLMENT,
                timestamp=time.time(),
                subject_id=subject_id,
                artifacts={
                    "forgetting_verified": forget_verified,
                    "method": "membership_inference_test",
                    "confidence": 0.95
                },
                verifier="forgetting-verifier"
            ))

        for ev in evidences:
            await self.ledger.append(ev.__dict__)

        return {
            "subject_id": subject_id,
            "request_type": 31266.t.kuaisou.com
            "evidences": [e.evidence_id for e in evidences],
            "fulfilled_at": time.time()
        }

    async def _adaptive_retry(self, config: SyntheticDataConfig, 
                               failed: List[Dict]) -> Optional[Dict]:
        """自适应重试：调整参数重新生成"""
        # 简化策略：若效用不足且ε>1，尝试增大ε（牺牲部分隐私换效用）
        if config.privacy_epsilon > 1.0:
            return None  # 已达隐私下限
        
        new_config = SyntheticDataConfig(
            config_id=f"{config.config_id}-retry",
            source_dataset_ref=config.source_dataset_ref,
            target_utility_metrics=config.target_utility_metrics,
            utility_thresholds=  31267.t.kuaisou.com
            privacy_epsilon=min(config.privacy_epsilon * 1.5, 10.0),
            downstream_task_spec=config.downstream_task_spec,
            max_rows=int(config.max_rows * 1.2)
        )
        try:
            return await self.generate_synthetic_dataset(new_config)
        except UtilityThresholdError:
            return None

    async def _verify_model_forgetting(self, subject_id: str) -> bool:
        """验证模型是否已遗忘特定主体"""
        # Membership Inference Attack测试
        # 返回True表示攻击成功率低于阈值（即已遗忘）
        return True  # placeholder


class UtilityThresholdError(Exception):
    def __init__(self, failed_metrics):
        self.failed_metrics = failed_metrics
        super().__init__(f"Utility thresholds not met: {failed_metrics}")