跨越"数据主权幻觉"：隐私计算联邦学习2.0、跨境数据可信流通与合规自动化实战

┌─────────────────────────────────────────────────────────────────────┐
│        2026 Trusted Data Circulation Engineering Architecture       │
├─────────────────────────────────────────────────────────────────────┤
│  [Application Layer: Federated Learning / Cross-border Analytics]   │
│      ↓                                                              │
│  [Layer 1: 可验证安全层] ← ZKP / Formal Verification / DUA-as-Code │
│   ├─ 密码学操作的正确性零知识证明                                     │
│   ├─ 数据使用协议的机器可执行编码                                     │
│   └─ 多方行为的不可抵赖审计日志                                       │
│      ↓                                                              │
│  [Layer 2: 自适应效能层] ← Adaptive Crypto / HW Acceleration / Tiering│
│   ├─ 数据敏感度驱动的安全等级动态分配                                  │
│   ├─ 硬件感知的密码学算法调度                                         │
│   └─ 安全-效用-性能帕累托优化                                         │
│      ↓                                                              │
│  [Layer 3: 合规自动化层] ← Regulation Graph / Auto-tagging / Audit  │
│   ├─ 多国法规的结构化知识图谱                                         │
│   ├─ 数据分类分级与合规标签自动生成                                    │
│   └─ 实时合规检查与跨境流通许可引擎                                    │
└─────────────────────────────────────────────────────────────────────┘

pip install pydantic fastapi opentelemetry-api petlib circomlibpy torch-federated
# 部署: OpenTelemetry Collector + IPFS (审计存证) + Redis (协议状态) + PostgreSQL (合规图谱) + GPU密码学加速卡

"""
verifiable_federation_engine.py - 可验证联邦学习与DUA执行引擎
技术栈: Pydantic / Circom (ZKP) / OpenTelemetry / Petlib
"""
from typing import Dict, List, Any, Optional, Tuple, Set
from pydantic import BaseModel, Field
from enum import Enum
import asyncio
import time
import uuid
import json
import hashlib
from dataclasses import dataclass, field
from contextlib import asynccontextmanager

class SecurityProtocol(str, Enum):
    HOMOMORPHIC_ENCRYPTION = "he"
    SECURE_MULTI_PARTY_COMPUTATION = "mpc"
    TRUSTED_EXECUTION_ENVIRONMENT = "tee"
    DIFFERENTIAL_PRIVACY = "dp"
    ZERO_KNOWLEDGE_PROOF = "zkp"

class DUAClauseType(str, Enum):
    PURPOSE_LIMITATION = "purpose_limitation"      # 用途限制
    RETENTION_PERIOD = "retention_period"          # 保留期限
    GEO_RESTRICTION = "geo_restriction"            # 地域限制
    RE_IDENTIFICATION_BAN = "re_id_ban"            # 禁止重识别
    AUDIT_RIGHT = "audit_right"                    # 审计权
    MODEL_OUTPUT_CONSTRAINT = "output_constraint"  # 模型输出约束

@dataclass
class DataUsageAgreement:
    """机器可读的数据使用协议"""
    dua_id: str
    parties: List[str]
    clauses: List[Dict[str, Any]]  # {type, params, enforceable}
    effective_date: float
    expiry_date: float
    signature_hashes: Dict[str, str]  # party -> sig hash

@dataclass
class FederationRound:
    """联邦学习轮次"""
    round_id: str
    session_id: 31273.t.kuaisou.com 
    participants: List[str]
    protocol: SecurityProtocol
    gradient_norm_bound: float
    noise_multiplier: float
    zkp_proof: Optional[str] = None
    dua_compliance_check: bool = True
    timestamp: float = field(default_factory=time.time)

class VerifiableFederationEngine:
    """可验证联邦学习引擎"""

    def __init__(self, crypto_backend, dua_registry, 
                 audit_ledger, otel_tracer):
        self.crypto = crypto_backend        # HE/MPC/ZKP后端
        self.dua_reg = dua_registry         # DUA注册与查询
        self.ledger = audit_ledger          # IPFS/QLDB审计账本
        self.tracer = otel_tracer
        self._active_sessions: Dict[str, Dict] = {}

    @asynccontextmanager
    async def run_federation_session(self, session_id: str,
                                      participants: List[str],
                                      dua_id: str):
        """启动受DUA约束的联邦会话"""
        # 加载并验证DUA
        dua = await self.dua_reg.get_active_dua(dua_id, participants)
        if not dua:
            raise DUAValidationError(f"No valid DUA for {participants}")

        self._active_sessions[session_id] = {
            "dua": dua,
            "rounds": [],
            "violations": []
        }

        # 发射会话开始事件
        await self._emit_audit("session_start", {
            "session_id": session_id,
            "dua_id": dua_id,
            "parties": participants,
            "clauses_count": len(dua.clauses)
        })

        try:
            yield session_id
        finally:
            # 会话结束，生成合规证明
            compliance_cert = await self._generate_compliance_certificate(session_id)
            await self._emit_audit("session_end", {
                "session_id": session_id,
                "rounds_completed": len(self._active_sessions[session_id]["rounds"]),
                "violations": len(self._active_sessions[session_id]["violations"]),
                "compliance_cert_hash": compliance_cert["hash"]
            })
            del self._active_sessions[session_id]

    async def execute_round(self, session_id: str,
                             gradients: Dict[str, bytes],
                             protocol: SecurityProtocol) -> Dict[str, Any]:
        """执行一轮带ZKP验证的安全聚合"""
        session = self._active_sessions.get(session_id)
        if not session:
            raise SessionNotFoundError(session_id)

        dua = session["dua"]
        round_id = f"rnd-{uuid.uuid4().hex[:8]}"

        # Step 1: DUA合规预检
        pre_check = await self._check_dua_compliance(session_id, gradients)
        if not pre_check["compliant"]:
            violation = {
                "round_id": 31274.t.kuaisou.com
                "clause": pre_check["violated_clause"],
                "detail": pre_check["detail"],
                "timestamp": time.time()
            }
            session["violations"].append(violation)
            raise DUAComplianceViolation(violation)

        # Step 2: 安全聚合 + ZKP生成
        with self.tracer.start_as_current_span("secure_aggregation") as span:
            span.set_attribute("federation.protocol", protocol.value)
            
            agg_result = await self.crypto.aggregate(
                gradients=gradients,
                protocol=protocol,
                norm_bound=session.get("gradient_norm_bound", 1.0)
            )
            
            # 生成聚合正确性的零知识证明
            zkp_proof = await self.crypto.generate_aggregation_zkp(
                inputs=list(gradients.values()),
                output=agg_result["aggregated_gradient"],
                protocol=protocol
            )
        
        # Step 3: 记录本轮
        fed_round = FederationRound(
            round_id=round_id,
            session_id=session_id,
            participants=list(gradients.keys()),
            protocol=protocol,
            gradient_norm_bound=session.get("gradient_norm_bound", 1.0),
            noise_multiplier=agg_result.get("noise_multiplier", 0.0),
            zkp_proof=zkp_proof,
            dua_compliance_check=True
        )
        session["rounds"].append(fed_round)

        # Step 4: 审计存证
        await self._emit_audit("round_complete", {
            "session_id": session_id,
            "round_id": round_id,
            "protocol": protocol.value,
            "participants_count": len(gradients),
            "zkp_proof_hash": hashlib.sha256(zkp_proof.encode()).hexdigest()[:16],
            "dua_compliant": 31275.t.kuaisou.com
        })

        return {
            "round_id": round_id,
            "aggregated_gradient": agg_result["aggregated_gradient"],
            "zkp_proof": zkp_proof,
            "verification_key": agg_result["verification_key"],
            "dua_compliant": True
        }

    async def _check_dua_compliance(self, session_id: str,
                                     gradients: Dict[str, bytes]) -> Dict[str, Any]:
        """检查本轮梯度是否符合DUA条款"""
        session = self._active_sessions[session_id]
        dua = session["dua"]
        
        for clause in dua.clauses:
            clause_type = DUAClauseType(clause["type"])
            
            if clause_type == DUAClauseType.MODEL_OUTPUT_CONSTRAINT:
                # 检查梯度范数是否超出约定（防止模型反推数据）
                max_norm = clause["params"].get("max_gradient_norm", 1.0)
                for party, grad in gradients.items():
                    norm = await self.crypto.compute_encrypted_norm(grad)
                    if norm > max_norm:
                        return {
                            "compliant": False,
                            "violated_clause": clause_type.value,
                            "detail": f"{party} gradient norm {norm:.3f} > {max_norm}"
                        }
            
            elif clause_type == DUAClauseType.RE_IDENTIFICATION_BAN:
                # 检查是否包含高维稀疏特征（易重识别）
                if await self.crypto.detect_sparse_features(gradients):
                    return {
                        "compliant": False,
                        "violated_clause": clause_type.value,
                        "detail": "High-dimensional sparse features detected in gradients"
                    }
        
        return {"compliant": True}

    async def _generate_compliance_certificate(self, session_id: str) -> Dict[str, Any]:
        """生成会话级合规证书"""
        session = self._active_sessions[session_id]
        rounds = session["rounds"]
        violations = session["violations"]
        
        # 汇总所有ZKP证明
        proof_hashes = [r.zkp_proof for r in rounds if r.zkp_proof]
        merkle_root = self._compute_merkle_root(proof_hashes)
        
        cert = {
            "session_id": session_id,
            "dua_id": session["dua"].dua_id,
            "total_rounds": len(rounds),
            "violations_count": len(violations),
            "proof_merkle_root": merkle_root,
            "generated_at": time.time(),
            "issuer": "verifiable_federation_engine_v2"
        }
        cert["hash"] = hashlib.sha256(json.dumps(cert, sort_keys=True).encode()).hexdigest()
        
        # 存入不可篡改账本
        await self.ledger.append(cert)
        return cert

    async def _emit_audit(self, event_type: str, data: Dict):
        await self.ledger.append({
            "event_type": event_type,
            "timestamp": time.time(),
            "data": data
        })

    def _compute_merkle_root(self, leaves: List[str]) -> str:
        if not leaves:
            return hashlib.sha256(b"empty").hexdigest()
        hashes = [hashlib.sha256(l.encode()).digest() for l in leaves]
        while len(hashes) > 1:
            next_level = []
            for i in range(0, len(hashes), 2):
                left = hashes[i]
                right = hashes[i+1] if i+1 < len(hashes) else left
                next_level.append(hashlib.sha256(left + right).digest())
            hashes = next_level
        return hashes[0].hex()


class DUAValidationError(Exception):
    pass

class SessionNotFoundError(Exception):
    pass

class DUAComplianceViolation(Exception):
    def __init__(self, violation: Dict):
        self.violation = violation
        super().__init__(json.dumps(violation))

"""
cross_border_compliance_engine.py - 跨境数据合规自动化引擎
技术栈: Pydantic / RDFLib / OpenTelemetry / Jinja2
"""
from typing import Dict, List, Any, Optional, Tuple, Set
from pydantic import BaseModel, Field
from enum import Enum
import asyncio
import time
import json
import hashlib
from dataclasses import dataclass, field

class Jurisdiction(str, Enum):
    CN = "CN"       # 中国
    EU = "EU"       # 欧盟
    US = "US"       # 美国
    SG = "SG"       # 新加坡
    JP = "JP"       # 日本

class DataCategory(str, Enum):
    PERSONAL_INFO = "personal_info"
    SENSITIVE_PERSONAL = "sensitive_personal"
    IMPORTANT_DATA = "important_data"
    CORE_DATA = "core_data"
    ANONYMIZED = "anonymized"
    SYNTHETIC = "synthetic"

class ProcessingPurpose(str, Enum):
    CLINICAL_RESEARCH = "clinical_research"
    FINANCIAL_RISK_MODELING = "financial_risk"
    SUPPLY_CHAIN_OPTIMIZATION = "supply_chain"
    AI_TRAINING = "ai_training"
    PUBLIC_HEALTH = "public_health"

@dataclass
class DataAssetTag:
    """机器可读数据标签"""
    asset_id: str
    category: DataCategory
    jurisdictions_of_origin: List[Jurisdiction]
    purposes_allowed: List[ProcessingPurpose]
    retention_days: int
    geo_restrictions: List[str]           # 禁止流向的国家/地区
    anonymization_method: Optional[str]
    consent_scope: Optional[str]          # 用户授权范围描述
    regulation_mappings: Dict[str, str]   # regulation_id -> article_ref
    tag_version: str
    generated_at: float

class CrossBorderComplianceEngine:
    """跨境数据合规自动化引擎"""

    # 各司法辖区对数据类别的出境规则（简化示例）
    REGULATION_RULES = {
        Jurisdiction.CN: {
            DataCategory.CORE_DATA: {"allowed": False},
            DataCategory.IMPORTANT_DATA: {"allowed": True, "requires": ["security_assessment"]},
            DataCategory.SENSITIVE_PERSONAL: {"allowed": True, "requires": ["consent", "impact_assessment"]},
            DataCategory.PERSONAL_INFO: {"allowed": True, "requires": ["consent_or_contract"]},
            DataCategory.ANONYMIZED: {"allowed": True, "requires": []},
        },
        Jurisdiction.EU: {
            DataCategory.SENSITIVE_PERSONAL: {"allowed": True, "requires": ["explicit_consent", "adequacy_or_safeguards"]},
            DataCategory.PERSONAL_INFO: {"allowed": True, "requires": ["legal_basis", "transfer_mechanism"]},
            DataCategory.ANONYMIZED: {"allowed": True, "requires": []},
        },
        # ... 其他辖区
    }

    TRANSFER_MECHANISMS = {
        "adequacy_decision": ["EU→JP", "EU→SG", "CN→HK"],
        "standard_contractual_clauses": ["EU→US", "EU→CN", "CN→EU"],
        "binding_corporate_rules": ["intra_group"],
        "security_assessment": ["CN_outbound_important"],
    }

    def __init__(self, regulation_graph, tag_registry, 
                 audit_stream, consent_db):
        self.reg_graph = regulation_graph     # RDF合规知识图谱
        self.tags = tag_registry              # 数据标签存储
        self.audit = audit_stream
        self.consent = consent_db             # 用户同意记录库

    async def evaluate_cross_border_transfer(
        self, asset_id: str, destination_jurisdiction: Jurisdiction,
        purpose: ProcessingPurpose
    ) -> Dict[str, Any]:
        """评估一次跨境数据流通的合规性"""
        tag = await self.tags.get(asset_id)
        if not tag:
            raise DataAssetNotFound(asset_id)

        result = {
            "asset_id": asset_id,
            "destination": destination_jurisdiction.value,
            "purpose": purpose.value,
            "compliant": True,
            "required_actions": [],
            "blocked_reasons": [],
            "regulation_references": [],
            "confidence_score": 1.0
        }

        # Check 1: 数据类别是否允许出境
        origin_rules = {}
        for origin in tag.jurisdictions_of_origin:
            rules = self.REGULATION_RULES.get(origin, {})
            cat_rule = rules.get(tag.category, {"allowed": False})
            origin_rules[origin.value] = cat_rule
            
            if not cat_rule.get("allowed", False):
                result["compliant"] = False
                result["blocked_reasons"].append(
                    f"{origin.value} prohibits export of {tag.category.value}"
                )

        # Check 2: 目的是否在授权范围内
        if purpose not in tag.purposes_allowed:
            result["compliant"] = False
            result["blocked_reasons"].append(
                f"Purpose '{purpose.value}' not in allowed purposes: {[p.value for p in tag.purposes_allowed]}"
            )

        # Check 3: 地域限制
        if destination_jurisdiction.value in tag.geo_restrictions:
            result["compliant"] = False
            result["blocked_reasons"].append(
                f"Destination {destination_jurisdiction.value} is geo-restricted"
            )

        # Check 4: 传输机制可用性
        if result["compliant"]:
            required = set()
            for origin, rule in origin_rules.items():
                required.update(rule.get("requires", []))
            
            available_mechanisms = self._find_applicable_transfer_mechanisms(
                tag.jurisdictions_of_origin, destination_jurisdiction, tag.category
            )
            
            unmet = required - set(available_mechanisms)
            if unmet:
                result["compliant"] = False
                result["blocked_reasons"].append(
                    f"No transfer mechanism for requirements: {unmet}"
                )
            else:
                result["required_actions"] = list(required)
                result["regulation_references"] = [
                    tag.regulation_mappings.get(r, "unknown") for r in required
                ]

        # Check 5: 同意有效性（如涉及个人数据）
        if tag.category in [DataCategory.PERSONAL_INFO, DataCategory.SENSITIVE_PERSONAL]:
            consent_valid = await self.consent.verify_consent(
                asset_id=asset_id,
                purpose=purpose,
                destination=destination_jurisdiction
            )
            if not consent_valid:
                result["compliant"] = False
                result["blocked_reasons"].append("Valid consent not found for this transfer")
                result["confidence_score"] = 0.3

        # 发射审计事件
        await self.audit.emit("transfer_evaluation", {
            "asset_id": 31276.t.kuaisou.com ,
            "destination": destination_jurisdiction.value,
            "compliant": result["compliant"],
            "blocked_reasons_count": len(result["blocked_reasons"]),
            "evaluation_timestamp": time.time()
        })

        return result

    async def auto_tag_data_asset(self, raw_metadata: Dict[str, Any]) -> DataAssetTag:
        """基于元数据自动生成合规标签"""
        # 调用分类模型识别数据类别
        category = await self._classify_data_category(raw_metadata)
        
        # 查询适用法规
        regulations = await self.reg_graph.query_applicable_regulations(
            category=category,
            jurisdictions=raw_metadata.get("jurisdictions", ["CN"])
        )
        
        # 推断允许用途与限制
        purposes = self._infer_allowed_purposes(category, raw_metadata)
        geo_restrictions = self._infer_geo_restrictions(category, regulations)
        retention = self._infer_retention(category, regulations)
        
        tag = DataAssetTag(
            asset_id=raw_metadata["asset_id"],
            category=category,
            jurisdictions_of_origin=[Jurisdiction(j) for j in raw_metadata.get("jurisdictions", ["CN"])],
            purposes_allowed=purposes,
            retention_days=retention,
            geo_restrictions=geo_restrictions,
            anonymization_method=raw_metadata.get("anonymization"),
            consent_scope=raw_metadata.get("consent_scope"),
            regulation_mappings={r["id"]: r["article"] for r in regulations},
            tag_version="2.0",
            generated_at=time.time()
        )
        
        await self.tags.upsert(tag)
        return tag

    def _find_applicable_transfer_mechanisms(
        self, origins: List[Jurisdiction], dest: Jurisdiction, 
        category: DataCategory
    ) -> Set[str]:
        mechanisms = set()
        for origin in origins:
            key = f"{origin.value}→{dest.value}"
            for mech, routes in self.TRANSFER_MECHANISMS.items():
                if key in routes or (category == DataCategory.IMPORTANT_DATA and mech == "security_assessment"):
                    mechanisms.add(mech)
        return mechanisms

    async def _classify_data_category(self, metadata: Dict) -> DataCategory:
        # 简化：实际应调用NLP分类模型
        schema = metadata.get("schema", "")
        if "id_card" in schema or "biometric" in schema:
            return DataCategory.SENSITIVE_PERSONAL
        elif "name" in schema or "phone" in schema:
            return DataCategory.PERSONAL_INFO
        elif "national_infrastructure" in metadata.get("tags", []):
            return DataCategory.IMPORTANT_DATA
        else:
            return DataCategory.ANONYMIZED

    def _infer_allowed_purposes(self, category: DataCategory, 
                                 metadata: Dict) -> List[ProcessingPurpose]:
        # 基于类别和元数据推断
        if category == DataCategory.ANONYMIZED:
            return list(ProcessingPurpose)
        elif category == DataCategory.SENSITIVE_PERSONAL:
            return [ProcessingPurpose.CLINICAL_RESEARCH, ProcessingPurpose.PUBLIC_HEALTH]
        else:
            return [ProcessingPurpose.AI_TRAINING, ProcessingPurpose.SUPPLY_CHAIN_OPTIMIZATION]

    def _infer_geo_restrictions(self, category: DataCategory, 
                                 regulations: List[Dict]) -> List[str]:
        restrictions = []
        for reg in regulations:
            restrictions.extend(reg.get("prohibited_destinations", []))
        return list(set(restrictions))

    def _infer_retention(self, category: DataCategory, 
                          regulations: List[Dict]) -> int:
        min_retention = 3650  # default 10 years
        for reg in regulations:
            max_days = reg.get("max_retention_days", 3650)
            min_retention = min(min_retention, max_days)
        return min_retention


class DataAssetNotFound(Exception):
    pass