class EUAIActCompliance:
    """
    欧盟 AI 法案合规框架
    """
    
    def __init__(self):
        self.risk_categories = {
            'unacceptable': ['social_scoring', 'manipulative_ai'],
            'high': ['biometric', 'critical_infrastructure', 'employment', 'education'],
            'limited': ['chatbots', 'emotion_recognition'],
            'minimal': ['spam_filters', 'games']
        }
    
    def classify_saas_product(self, product):
        """
        对 SaaS 产品进行风险分类
        """
        features = product.get_features()
        
        # 检查是否涉及高风险类别
        for feature in features:
            if feature.category in self.risk_categories['high']:
                return {
                    'risk_level': 'high',
                    'requirements': [
                        'conformity_assessment',
                        'risk_management_system',
                        'data_governance',
                        'technical_documentation',
                        'transparency',
                        'human_oversight',
                        'accuracy_robustness'
                    ],
                    'penalties': {
                        'minor': 'up_to_10M_EUR_or_2%_revenue',
                        'major': 'up_to_20M_EUR_or_4%_revenue',
                        'prohibited': 'up_to_35M_EUR_or_7%_revenue'
                    }
                }
        
        return {'risk_level': 'minimal', 'requirements': []}

class USPrivacyLandscape:
    """
    美国隐私法规环境
    """
    
    def __init__(self):
        self.federal_laws = {
            'HIPAA': 'healthcare',
            'GLBA': 'financial',
            'COPPA': 'children',
            'FERPA': 'education'
        }
        
        self.state_laws = {
            'California': ['CCPA', 'CPRA'],
            'Virginia': 'VCDPA',
            'Colorado': 'CPA',
            'Connecticut': 'CTDPA',
            'Utah': 'UCPA',
            'Texas': 'TDPSA',
            'Oregon': 'OCPA',
            'Montana': 'MTCDPA',
            'Florida': 'FDBR'
        }
    
    def get_compliance_requirements(self, business):
        """
        获取适用于企业的合规要求
        """
        requirements = []
        
        # 联邦法律
        if business.industry == 'healthcare':
            requirements.append('HIPAA')
        elif business.industry == 'financial':
            requirements.append('GLBA')
        
        # 州级法律
        for state in business.operating_states:
            if state in self.state_laws:
                requirements.extend(self.state_laws[state])
        
        # 通用要求
        common_requirements = [
            'privacy_policy',
            'cookie_consent',
            'data_subject_rights',
            'data_breach_notification',
            'vendor_management'
        ]
        
        return {
            'applicable_laws': requirements,
            'common_requirements': common_requirements
        }

class ChinaPIPLCompliance:
    """
    中国个人信息保护法合规
    """
    
    def __init__(self):
        self.key_principles = [
            'legality',           # 合法性
            'necessity',          # 必要性
            'transparency',       # 透明性
            'purpose_limitation', # 目的限制
            'data_minimization'   # 数据最小化
        ]
    
    def cross_border_transfer_requirements(self, data_type, volume):
        """
        跨境数据传输要求
        """
        if data_type == 'critical' or volume > 1000000:
            return {
                'requirement': 'security_assessment',
                'authority': 'Cyberspace Administration of China',
                'timeline': '3-6 months'
            }
        elif volume > 100000:
            return {
                'requirement': 'standard_contract',
                'filing': 'required'
            }
        else:
            return {
                'requirement': 'certification',
                'scope': 'limited'
            }

class TrainingDataPrivacy:
    """
    AI 训练数据隐私保护
    """
    
    def __init__(self):
        self.techniques = {
            'differential_privacy': DifferentialPrivacy(),
            'federated_learning': FederatedLearning(),
            'synthetic_data': SyntheticDataGenerator()
        }
    
    def protect_training_data(self, dataset, privacy_level='high'):
        """
        保护训练数据隐私
        """
        if privacy_level == 'high':
            # 使用差分隐私
            protected_data = self.techniques['differential_privacy'].apply(
                dataset,
                epsilon=0.1,  # 强隐私保护
                delta=1e-5
            )
        elif privacy_level == 'medium':
            # 使用联邦学习
            protected_data = self.techniques['federated_learning'].train(
                dataset,
                rounds=100
            )
        else:
            # 使用合成数据
            protected_data = self.techniques['synthetic_data'].generate(
                dataset,
                fidelity=0.95
            )
        
        return protected_data

class InferencePrivacy:
    """
    模型推理过程中的隐私保护
    """
    
    def __init__(self):
        self.homomorphic_encryption = HomomorphicEncryption()
        self.secure_enclave = SecureEnclave()
    
    def private_inference(self, model, user_data):
        """
        隐私保护的模型推理
        """
        # 同态加密推理
        encrypted_data = self.homomorphic_encryption.encrypt(user_data)
        encrypted_result = model.predict(encrypted_data)
        result = self.homomorphic_encryption.decrypt(encrypted_result)
        
        # 或使用安全飞地
        with self.secure_enclave:
            result = model.predict(user_data)
            # 数据在飞地内处理，外部无法访问
        
        return result

class ModelMemorizationDetector:
    """
    检测模型是否记忆了训练数据
    """
    
    def __init__(self):
        self.membership_inference = MembershipInferenceAttack()
        self.extraction_attack = ModelExtractionAttack()
    
    def test_privacy_leakage(self, model, training_data):
        """
        测试模型的隐私泄露风险
        """
        # 成员推理攻击
        membership_risk = self.membership_inference.attack(
            model,
            training_data
        )
        
        # 模型提取攻击
        extraction_risk = self.extraction_attack.attack(
            model,
            n_queries=10000
        )
        
        # 训练数据提取
        extraction_success = self.attempt_data_extraction(
            model,
            n_attempts=1000
        )
        
        return {
            'membership_inference_accuracy': membership_risk.accuracy,
            'extraction_fidelity': extraction_risk.fidelity,
            'data_extraction_success_rate': extraction_success.rate,
            'risk_level': self.calculate_risk_level(
                membership_risk,
                extraction_risk,
                extraction_success
            )
        }

class DifferentialPrivacy:
    """
    差分隐私实现
    """
    
    def __init__(self, epsilon=1.0, delta=1e-5):
        self.epsilon = epsilon  # 隐私预算
        self.delta = delta
    
    def add_noise(self, data, sensitivity):
        """
        添加拉普拉斯噪声
        """
        scale = sensitivity / self.epsilon
        noise = np.random.laplace(0, scale, data.shape)
        return data + noise
    
    def private_aggregation(self, data, query_type='mean'):
        """
        隐私保护的聚合查询
        """
        if query_type == 'mean':
            sensitivity = 1.0 / len(data)
            result = np.mean(data)
            return self.add_noise(result, sensitivity)
        
        elif query_type == 'sum':
            sensitivity = 1.0
            result = np.sum(data)
            return self.add_noise(result, sensitivity)
        
        elif query_type == 'count':
            sensitivity = 1.0
            result = len(data)
            return self.add_noise(result, sensitivity)
    
    def privacy_accounting(self, queries):
        """
        隐私预算管理
        """
        total_epsilon = sum(q.epsilon for q in queries)
        
        if total_epsilon > self.epsilon:
            raise PrivacyBudgetExceeded(
                f"Total epsilon {total_epsilon} exceeds budget {self.epsilon}"
            )
        
        return total_epsilon

class FederatedLearning:
    """
    联邦学习实现
    """
    
    def __init__(self, n_clients=10):
        self.n_clients = n_clients
        self.clients = [Client(i) for i in range(n_clients)]
        self.server = Server()
    
    def train(self, global_model, n_rounds=100):
        """
        联邦学习训练过程
        """
        for round_num in range(n_rounds):
            # 选择参与客户端
            selected_clients = self.select_clients(
                fraction=0.3
            )
            
            # 客户端本地训练
            client_updates = []
            for client in selected_clients:
                # 下载全局模型
                client.set_model(global_model)
                
                # 本地训练
                local_update = client.train(
                    local_data=client.data,
                    epochs=5
                )
                
                client_updates.append(local_update)
            
            # 服务器聚合
            global_model = self.server.aggregate(
                client_updates,
                aggregation_method='fedavg'
            )
            
            # 评估
            if round_num % 10 == 0:
                metrics = self.evaluate(global_model)
                print(f"Round {round_num}: {metrics}")
        
        return global_model
    
    def select_clients(self, fraction=0.3):
        """
        选择参与训练的客户端
        """
        n_selected = int(self.n_clients * fraction)
        return random.sample(self.clients, n_selected)

class HomomorphicEncryption:
    """
    同态加密实现
    """
    
    def __init__(self, scheme='CKKS'):
        self.scheme = scheme
        self.context = self.setup_context()
    
    def setup_context(self):
        """
        设置加密上下文
        """
        if self.scheme == 'CKKS':
            # CKKS 方案：支持浮点数运算
            params = {
                'poly_modulus_degree': 8192,
                'coeff_modulus': [60, 40, 40, 60],
                'scale': 2**40
            }
            return CKKSContext(params)
        
        elif self.scheme == 'BFV':
            # BFV 方案：支持整数运算
            params = {
                'poly_modulus_degree': 8192,
                'plain_modulus': 1024
            }
            return BFVContext(params)
    
    def encrypt(self, plaintext):
        """
        加密数据
        """
        return self.context.encrypt(plaintext)
    
    def decrypt(self, ciphertext):
        """
        解密数据
        """
        return self.context.decrypt(ciphertext)
    
    def homomorphic_operations(self, encrypted_data):
        """
        在加密数据上执行运算
        """
        # 同态加法
        encrypted_sum = encrypted_data[0]
        for data in encrypted_data[1:]:
            encrypted_sum = self.context.add(encrypted_sum, data)
        
        # 同态乘法
        encrypted_product = encrypted_data[0]
        for data in encrypted_data[1:]:
            encrypted_product = self.context.multiply(
                encrypted_product,
                data
            )
        
        return encrypted_sum, encrypted_product

class DataCatalog:
    """
    统一数据目录
    """
    
    def __init__(self):
        self.metadata_store = MetadataStore()
        self.lineage_tracker = LineageTracker()
        self.classifier = DataClassifier()
    
    def register_dataset(self, dataset):
        """
        注册数据集
        """
        # 自动分类
        classification = self.classifier.classify(dataset)
        
        # 提取元数据
        metadata = {
            'name': dataset.name,
            'owner': dataset.owner,
            'schema': dataset.schema,
            'row_count': dataset.row_count,
            'last_updated': dataset.last_updated,
            'classification': classification,
            'pii_fields': self.detect_pii(dataset),
            'retention_policy': self.get_retention_policy(classification),
            'access_controls': self.get_access_controls(classification)
        }
        
        # 存储元数据
        self.metadata_store.save(dataset.id, metadata)
        
        # 追踪数据血缘
        self.lineage_tracker.track(dataset)
        
        return metadata
    
    def detect_pii(self, dataset):
        """
        检测个人身份信息
        """
        pii_fields = []
        
        for column in dataset.columns:
            # 基于规则检测
            if self.rule_based_pii_detection(column):
                pii_fields.append({
                    'column': column.name,
                    'type': self.classify_pii_type(column),
                    'confidence': 0.9
                })
            
            # 基于 ML 检测
            elif self.ml_based_pii_detection(column):
                pii_fields.append({
                    'column': column.name,
                    'type': self.classify_pii_type(column),
                    'confidence': 0.8
                })
        
        return pii_fields
    
    def track_access(self, dataset_id, user, action):
        """
        追踪数据访问
        """
        access_log = {
            'timestamp': datetime.now(),
            'dataset_id': dataset_id,
            'user': user,
            'action': action,
            'ip_address': request.remote_addr,
            'purpose': request.headers.get('X-Access-Purpose')
        }
        
        self.metadata_store.log_access(access_log)
        
        # 异常检测
        if self.detect_anomaly(access_log):
            self.alert_security_team(access_log)

class DataLineageTracker:
    """
    数据血缘追踪
    """
    
    def __init__(self):
        self.graph = LineageGraph()
    
    def track(self, dataset):
        """
        追踪数据集的血缘
        """
        # 添加节点
        self.graph.add_node(dataset.id, {
            'name': dataset.name,
            'type': 'dataset'
        })
        
        # 追踪上游
        for source in dataset.sources:
            self.graph.add_edge(source.id, dataset.id, {
                'transformation': dataset.transformation
            })
        
        # 追踪下游
        for dependent in dataset.dependents:
            self.graph.add_edge(dataset.id, dependent.id, {
                'transformation': dependent.transformation
            })
    
    def impact_analysis(self, dataset_id):
        """
        影响分析
        """
        # 获取所有下游数据集
        downstream = self.graph.get_downstream(dataset_id)
        
        # 计算影响范围
        impact = {
            'direct': len([d for d in downstream if d.distance == 1]),
            'indirect': len([d for d in downstream if d.distance > 1]),
            'total': len(downstream),
            'affected_datasets': [d.id for d in downstream]
        }
        
        return impact
    
    def compliance_check(self, dataset_id, regulation):
        """
        合规性检查
        """
        # 获取数据血缘
        lineage = self.graph.get_full_lineage(dataset_id)
        
        # 检查每个节点
        violations = []
        for node in lineage:
            if not self.check_node_compliance(node, regulation):
                violations.append({
                    'dataset': node.id,
                    'violation': self.get_violation_details(node, regulation)
                })
        
        return violations

class AutomatedComplianceChecker:
    """
    自动化合规检查
    """
    
    def __init__(self):
        self.rules = self.load_compliance_rules()
        self.scanner = ComplianceScanner()
    
    def check(self, system):
        """
        检查系统合规性
        """
        results = []
        
        for rule in self.rules:
            # 扫描系统
            findings = self.scanner.scan(system, rule)
            
            # 评估合规性
            compliance_status = self.evaluate(findings, rule)
            
            results.append({
                'rule': rule.id,
                'name': rule.name,
                'status': compliance_status.status,
                'findings': findings,
                'recommendations': self.generate_recommendations(
                    findings,
                    rule
                )
            })
        
        return {
            'overall_status': self.calculate_overall_status(results),
            'results': results,
            'report': self.generate_report(results)
        }
    
    def load_compliance_rules(self):
        """
        加载合规规则
        """
        rules = []
        
        # GDPR 规则
        rules.extend([
            ComplianceRule(
                id='GDPR-001',
                name='Data Processing Lawful Basis',
                check=self.check_lawful_basis
            ),
            ComplianceRule(
                id='GDPR-002',
                name='Data Subject Rights',
                check=self.check_data_subject_rights
            ),
            ComplianceRule(
                id='GDPR-003',
                name='Data Protection Impact Assessment',
                check=self.check_dpia
            )
        ])
        
        # CCPA 规则
        rules.extend([
            ComplianceRule(
                id='CCPA-001',
                name='Right to Know',
                check=self.check_right_to_know
            ),
            ComplianceRule(
                id='CCPA-002',
                name='Right to Delete',
                check=self.check_right_to_delete
            ),
            ComplianceRule(
                id='CCPA-003',
                name='Right to Opt-Out',
                check=self.check_opt_out
            )
        ])
        
        return rules

class ZeroTrustArchitecture:
    """
    零信任架构
    """
    
    def __init__(self):
        self.identity_provider = IdentityProvider()
        self.policy_engine = PolicyEngine()
        self.access_gateway = AccessGateway()
    
    def authenticate_request(self, request):
        """
        认证请求
        """
        # 验证身份
        identity = self.identity_provider.verify(
            request.credentials
        )
        
        if not identity:
            raise AuthenticationError("Invalid credentials")
        
        # 评估策略
        policy_decision = self.policy_engine.evaluate(
            identity=identity,
            resource=request.resource,
            action=request.action,
            context=request.context
        )
        
        if not policy_decision.allowed:
            raise AuthorizationError(
                f"Access denied: {policy_decision.reason}"
            )
        
        # 记录访问
        self.log_access(identity, request, policy_decision)
        
        return policy_decision
    
    def continuous_verification(self, session):
        """
        持续验证
        """
        while session.active:
            # 定期检查
            if self.should_reverify(session):
                # 重新验证身份
                identity = self.identity_provider.reverify(
                    session.identity
                )
                
                # 重新评估策略
                policy_decision = self.policy_engine.evaluate(
                    identity=identity,
                    resource=session.resource,
                    action=session.current_action,
                    context=session.context
                )
                
                if not policy_decision.allowed:
                    # 终止会话
                    session.terminate(
                        reason="Policy violation detected"
                    )
            
            time.sleep(60)  # 每分钟检查一次

class DataMinimization:
    """
    数据最小化实现
    """
    
    def __init__(self):
        self.field_analyzer = FieldAnalyzer()
        self.retention_manager = RetentionManager()
    
    def minimize_collection(self, data_requirements, purpose):
        """
        最小化数据收集
        """
        # 分析必需字段
        required_fields = self.field_analyzer.analyze(
            data_requirements,
            purpose
        )
        
        # 只收集必需字段
        minimized_requirements = {
            'fields': required_fields,
            'collection_method': 'explicit_consent',
            'retention_period': self.calculate_retention(purpose)
        }
        
        return minimized_requirements
    
    def minimize_storage(self, dataset, purpose):
        """
        最小化数据存储
        """
        # 识别非必要字段
        non_essential = self.field_analyzer.identify_non_essential(
            dataset,
            purpose
        )
        
        # 删除或匿名化
        for field in non_essential:
            if field.can_delete:
                dataset.drop_column(field.name)
            else:
                dataset.anonymize_column(field.name)
        
        # 设置保留期限
        retention_period = self.retention_manager.get_period(purpose)
        dataset.set_retention(retention_period)
        
        return dataset
    
    def minimize_processing(self, data, operation):
        """
        最小化数据处理
        """
        # 只处理必需的数据
        minimal_data = self.extract_minimal_data(
            data,
            operation.required_fields
        )
        
        # 使用临时数据
        if operation.can_use_temporary:
            minimal_data = self.create_temporary_copy(minimal_data)
        
        return minimal_data

class DataAccessRight:
    """
    数据访问权实现
    """
    
    def __init__(self):
        self.data_locator = DataLocator()
        self.report_generator = ReportGenerator()
    
    def handle_access_request(self, user_id, request):
        """
        处理数据访问请求
        """
        # 验证身份
        if not self.verify_identity(user_id, request.credentials):
            raise AuthenticationError("Identity verification failed")
        
        # 定位用户数据
        user_data = self.data_locator.find_all(user_id)
        
        # 生成报告
        report = self.report_generator.generate(
            user_data,
            format=request.format,  # JSON, CSV, PDF
            include_metadata=True
        )
        
        # 记录请求
        self.log_request(user_id, 'access', request)
        
        return {
            'report': report,
            'data_categories': self.categorize_data(user_data),
            'processing_purposes': self.get_purposes(user_data),
            'retention_periods': self.get_retention_periods(user_data),
            'third_party_sharing': self.get_third_party_sharing(user_data)
        }

class DataDeletionRight:
    """
    数据删除权实现
    """
    
    def __init__(self):
        self.data_locator = DataLocator()
        self.deletion_executor = DeletionExecutor()
        self.verification_system = VerificationSystem()
    
    def handle_deletion_request(self, user_id, request):
        """
        处理数据删除请求
        """
        # 验证身份
        if not self.verify_identity(user_id, request.credentials):
            raise AuthenticationError("Identity verification failed")
        
        # 检查是否可以删除
        deletion_check = self.check_deletion_eligibility(user_id)
        
        if not deletion_check.eligible:
            return {
                'status': 'rejected',
                'reason': deletion_check.reason,
                'alternatives': deletion_check.alternatives
            }
        
        # 定位所有数据
        user_data = self.data_locator.find_all(user_id)
        
        # 执行删除
        deletion_result = self.deletion_executor.execute(
            user_data,
            method='secure_delete',  # 安全删除
            include_backups=True,    # 包括备份
            notify_third_parties=True  # 通知第三方
        )
        
        # 验证删除
        verification = self.verification_system.verify(
            user_id,
            deletion_result
        )
        
        # 生成证明
        certificate = self.generate_deletion_certificate(
            user_id,
            deletion_result,
            verification
        )
        
        return {
            'status': 'completed',
            'certificate': certificate,
            'deleted_data': deletion_result.summary,
            'retained_data': deletion_result.exceptions,
            'retention_reasons': deletion_result.retention_reasons
        }
    
    def check_deletion_eligibility(self, user_id):
        """
        检查删除资格
        """
        # 检查法律保留要求
        legal_holds = self.check_legal_holds(user_id)
        if legal_holds:
            return {
                'eligible': False,
                'reason': 'Legal hold in place',
                'alternatives': ['restrict_processing', 'anonymize']
            }
        
        # 检查合同义务
        contracts = self.check_active_contracts(user_id)
        if contracts:
            return {
                'eligible': False,
                'reason': 'Active contracts require data retention',
                'alternatives': ['restrict_processing']
            }
        
        return {'eligible': True}

class DataPortabilityRight:
    """
    数据可携带权实现
    """
    
    def __init__(self):
        self.data_extractor = DataExtractor()
        self.format_converter = FormatConverter()
        self.transfer_system = TransferSystem()
    
    def handle_portability_request(self, user_id, request):
        """
        处理数据可携带请求
        """
        # 提取用户数据
        user_data = self.data_extractor.extract(
            user_id,
            scope=request.scope  # 'all' or specific categories
        )
        
        # 转换为标准格式
        portable_data = self.format_converter.convert(
            user_data,
            target_format=request.format,  # JSON, CSV, XML
            include_metadata=True,
            machine_readable=True
        )
        
        # 直接传输（如果请求）
        if request.direct_transfer:
            transfer_result = self.transfer_system.transfer(
                portable_data,
                destination=request.destination_service,
                secure=True
            )
            
            return {
                'status': 'transferred',
                'destination': request.destination_service,
                'transfer_id': transfer_result.id
            }
        
        # 提供下载
        download_url = self.generate_download_url(
            portable_data,
            expiry_hours=24
        )
        
        return {
            'status': 'ready',
            'download_url': download_url,
            'format': request.format,
            'size': len(portable_data),
            'expiry': '24 hours'
        }

class ConsentManagement:
    """
    同意管理
    """
    
    def __init__(self):
        self.consent_store = ConsentStore()
        self.purpose_registry = PurposeRegistry()
    
    def request_consent(self, user_id, purposes):
        """
        请求用户同意
        """
        consent_request = {
            'user_id': user_id,
            'purposes': [],
            'timestamp': datetime.now(),
            'version': '2.0'
        }
        
        for purpose in purposes:
            consent_request['purposes'].append({
                'id': purpose.id,
                'name': purpose.name,
                'description': purpose.description,
                'data_categories': purpose.data_categories,
                'retention_period': purpose.retention_period,
                'third_parties': purpose.third_parties,
                'optional': purpose.optional
            })
        
        return consent_request
    
    def record_consent(self, user_id, consent_response):
        """
        记录用户同意
        """
        consent_record = {
            'user_id': user_id,
            'timestamp': datetime.now(),
            'purposes': consent_response.purposes,
            'method': consent_response.method,  # 'explicit', 'implicit'
            'version': consent_response.version,
            'ip_address': request.remote_addr,
            'user_agent': request.user_agent
        }
        
        # 存储同意记录
        self.consent_store.save(consent_record)
        
        # 更新用户偏好
        self.update_user_preferences(user_id, consent_response)
        
        # 通知相关系统
        self.notify_systems(user_id, consent_response)
        
        return consent_record
    
    def check_consent(self, user_id, purpose_id):
        """
        检查用户同意
        """
        consent = self.consent_store.get_latest(user_id, purpose_id)
        
        if not consent:
            return {'has_consent': False, 'reason': 'No consent found'}
        
        if consent.withdrawn:
            return {'has_consent': False, 'reason': 'Consent withdrawn'}
        
        if consent.expired:
            return {'has_consent': False, 'reason': 'Consent expired'}
        
        return {
            'has_consent': True,
            'consent_details': consent,
            'granted_at': consent.timestamp,
            'expires_at': consent.expiry
        }
    
    def withdraw_consent(self, user_id, purpose_id):
        """
        撤回同意
        """
        # 标记同意为已撤回
        self.consent_store.withdraw(user_id, purpose_id)
        
        # 触发数据处理
        self.handle_consent_withdrawal(user_id, purpose_id)
        
        # 通知相关系统
        self.notify_withdrawal(user_id, purpose_id)
        
        return {'status': 'withdrawn', 'purpose_id': purpose_id}

class PrivacyByDesign:
    """
    隐私设计原则
    """
    
    def __init__(self):
        self.principles = [
            'proactive_not_reactive',
            'privacy_as_default',
            'privacy_embedded',
            'full_functionality',
            'end_to_end_security',
            'visibility_transparency',
            'user_centric'
        ]
    
    def apply_to_feature(self, feature_design):
        """
        将隐私设计应用于功能设计
        """
        privacy_enhanced_design = feature_design.copy()
        
        # 主动而非被动
        privacy_enhanced_design['privacy_impact_assessment'] = \
            self.conduct_pia(feature_design)
        
        # 隐私作为默认
        privacy_enhanced_design['default_settings'] = \
            self.configure_privacy_defaults(feature_design)
        
        # 隐私嵌入
        privacy_enhanced_design['privacy_controls'] = \
            self.embed_privacy_controls(feature_design)
        
        # 端到端安全
        privacy_enhanced_design['security_measures'] = \
            self.design_security_measures(feature_design)
        
        # 可见性和透明性
        privacy_enhanced_design['transparency_features'] = \
            self.add_transparency_features(feature_design)
        
        # 以用户为中心
        privacy_enhanced_design['user_controls'] = \
            self.add_user_controls(feature_design)
        
        return privacy_enhanced_design

class ComplianceTraining:
    """
    合规培训
    """
    
    def __init__(self):
        self.training_modules = {
            'basic': BasicPrivacyTraining(),
            'advanced': AdvancedPrivacyTraining(),
            'role_specific': RoleSpecificTraining()
        }
    
    def assign_training(self, employee):
        """
        分配培训
        """
        required_modules = []
        
        # 基础培训（所有员工）
        required_modules.append('basic')
        
        # 高级培训（处理敏感数据的员工）
        if employee.handles_sensitive_data:
            required_modules.append('advanced')
        
        # 角色特定培训
        if employee.role in ['developer', 'data_scientist', 'product_manager']:
            required_modules.append('role_specific')
        
        # 分配培训
        for module in required_modules:
            self.training_modules[module].assign(employee)
        
        return {
            'employee': employee.id,
            'assigned_modules': required_modules,
            'deadline': self.calculate_deadline(required_modules)
        }
    
    def track_completion(self, employee):
        """
        跟踪完成情况
        """
        completion_status = {}
        
        for module_name, module in self.training_modules.items():
            status = module.get_completion_status(employee)
            completion_status[module_name] = status
        
        return completion_status