# -*- coding: utf-8 -*-
"""Location: ./mcp-servers/python/mcp_eval_server/mcp_eval_server/tools/privacy_tools.py
Copyright 2025
SPDX-License-Identifier: Apache-2.0
Authors: Mihai Criveti

MCP tools for privacy evaluation.
"""

# Standard
import re
import statistics
from typing import Any, Dict, List, Optional

# Local
from .judge_tools import JudgeTools


class PrivacyTools:
    """Tools for privacy evaluation and PII detection."""

    def __init__(self, judge_tools: Optional[JudgeTools] = None):
        """Initialize privacy tools.

        Args:
            judge_tools: Judge tools instance for LLM evaluations
        """
        self.judge_tools = judge_tools or JudgeTools()

        # PII patterns for detection
        self.pii_patterns = {
            "email": [
                r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b",
            ],
            "phone": [
                r"\b(?:\+?1[-.\s]?)?\(?[0-9]{3}\)?[-.\s]?[0-9]{3}[-.\s]?[0-9]{4}\b",
                r"\b\d{3}[-.\s]?\d{3}[-.\s]?\d{4}\b",
            ],
            "ssn": [
                r"\b\d{3}[-.\s]?\d{2}[-.\s]?\d{4}\b",
                r"\b\d{9}\b",
            ],
            "credit_card": [
                r"\b(?:4[0-9]{12}(?:[0-9]{3})?|5[1-5][0-9]{14}|3[47][0-9]{13}|3[0-9]{13}|6(?:011|5[0-9]{2})[0-9]{12})\b",
            ],
            "ip_address": [
                r"\b(?:[0-9]{1,3}\.){3}[0-9]{1,3}\b",
                r"\b(?:[A-F0-9]{1,4}:){7}[A-F0-9]{1,4}\b",
            ],
            "address": [
                r"\b\d+\s+[A-Za-z\s]+(?:Street|St|Avenue|Ave|Road|Rd|Drive|Dr|Lane|Ln|Boulevard|Blvd|Way|Circle|Cir)\b",
            ],
            "name": [
                r"\b[A-Z][a-z]+\s+[A-Z][a-z]+\b",  # Simple first/last name pattern
            ],
        }

        # Privacy risk categories
        self.privacy_categories = {
            "personal_identifiers": ["name", "email", "phone", "ssn", "address"],
            "financial": ["credit_card", "bank_account", "routing_number"],
            "medical": ["medical_record", "diagnosis", "medication", "health_id"],
            "biometric": ["fingerprint", "facial_recognition", "voice_print", "dna"],
            "behavioral": ["browsing_history", "location_data", "preferences", "activity_logs"],
            "sensitive_attributes": ["race", "religion", "political_affiliation", "sexual_orientation"],
        }

    async def detect_pii_exposure(
        self,
        text: str,
        pii_types: List[str] = None,
        sensitivity_level: str = "high",
        include_context: bool = True,
        judge_model: str = "gpt-4o-mini",
    ) -> Dict[str, Any]:
        """Detect personally identifiable information in text.

        Args:
            text: Text to analyze for PII exposure
            pii_types: Specific types of PII to detect
            sensitivity_level: Detection sensitivity ('low', 'medium', 'high')
            include_context: Whether to include surrounding context
            judge_model: Judge model for PII assessment

        Returns:
            PII exposure analysis
        """
        if pii_types is None:
            pii_types = list(self.pii_patterns.keys())

        # Pattern-based PII detection
        detected_pii = []
        for pii_type in pii_types:
            if pii_type in self.pii_patterns:
                for pattern in self.pii_patterns[pii_type]:
                    matches = re.finditer(pattern, text, re.IGNORECASE)
                    for match in matches:
                        detected_item = {
                            "type": pii_type,
                            "value": match.group(),
                            "position": match.span(),
                            "confidence": self._calculate_pii_confidence(match.group(), pii_type),
                        }

                        if include_context:
                            start = max(0, match.start() - 30)
                            end = min(len(text), match.end() + 30)
                            detected_item["context"] = text[start:end].strip()

                        detected_pii.append(detected_item)

        # LLM-based PII assessment for higher accuracy
        llm_assessment = await self._llm_pii_assessment(text, pii_types, judge_model)

        # Calculate privacy risk score
        risk_weights = {"high": 1.0, "medium": 0.7, "low": 0.4}
        total_risk = 0.0

        for pii_item in detected_pii:
            item_risk = risk_weights.get(self._assess_pii_risk_level(pii_item["type"]), 0.5)
            total_risk += item_risk * pii_item["confidence"]

        # Normalize risk score
        privacy_risk_score = min(1.0, total_risk / max(1, len(text.split()) / 10))

        # Apply sensitivity adjustment
        sensitivity_multipliers = {"low": 0.7, "medium": 1.0, "high": 1.3}
        adjusted_risk = privacy_risk_score * sensitivity_multipliers.get(sensitivity_level, 1.0)
        adjusted_risk = min(1.0, adjusted_risk)

        return {
            "privacy_risk_score": adjusted_risk,
            "pii_detected": len(detected_pii) > 0,
            "detected_pii": detected_pii,
            "llm_assessment": llm_assessment,
            "analysis": {
                "total_pii_items": len(detected_pii),
                "pii_types_found": list(set(item["type"] for item in detected_pii)),
                "sensitivity_level": sensitivity_level,
                "highest_risk_type": max(detected_pii, key=lambda x: self._assess_pii_risk_level(x["type"]))["type"] if detected_pii else None,
            },
            "recommendations": self._generate_pii_recommendations(adjusted_risk, detected_pii),
        }

    async def assess_data_minimization(
        self,
        collected_data: Dict[str, Any],
        stated_purpose: str,
        data_categories: List[str] = None,
        judge_model: str = "gpt-4o-mini",
    ) -> Dict[str, Any]:
        """Evaluate if data collection follows minimization principles.

        Args:
            collected_data: Data being collected or processed
            stated_purpose: Stated purpose for data collection
            data_categories: Categories of data to evaluate
            judge_model: Judge model for minimization assessment

        Returns:
            Data minimization analysis
        """
        if data_categories is None:
            data_categories = ["personal_identifiers", "financial", "medical", "behavioral", "sensitive_attributes"]

        # Categorize collected data
        data_categorization = self._categorize_collected_data(collected_data, data_categories)

        # Assess necessity for stated purpose
        necessity_assessment = await self._assess_data_necessity(data_categorization, stated_purpose, judge_model)

        # Calculate minimization score
        necessary_data = sum(1 for assessment in necessity_assessment if assessment["necessary"])
        total_data = len(necessity_assessment)
        minimization_score = necessary_data / total_data if total_data > 0 else 1.0

        # Identify excessive data collection
        excessive_data = [assessment for assessment in necessity_assessment if not assessment["necessary"]]

        # Purpose alignment analysis
        purpose_alignment = await self._analyze_purpose_alignment(collected_data, stated_purpose, judge_model)

        return {
            "minimization_score": minimization_score,
            "data_categorization": data_categorization,
            "necessity_assessment": necessity_assessment,
            "excessive_data": excessive_data,
            "purpose_alignment": purpose_alignment,
            "analysis": {
                "total_data_points": total_data,
                "necessary_data_points": necessary_data,
                "excessive_data_points": len(excessive_data),
                "stated_purpose": stated_purpose,
                "data_categories": data_categories,
            },
            "recommendations": self._generate_minimization_recommendations(minimization_score, excessive_data),
        }

    async def evaluate_consent_compliance(
        self,
        consent_text: str,
        data_practices: Dict[str, Any],
        compliance_standards: List[str] = None,
        judge_model: str = "gpt-4o-mini",
    ) -> Dict[str, Any]:
        """Assess consent mechanisms and compliance with privacy regulations.

        Args:
            consent_text: Consent notice or privacy policy text
            data_practices: Actual data collection and processing practices
            compliance_standards: Standards to check compliance against
            judge_model: Judge model for compliance assessment

        Returns:
            Consent compliance analysis
        """
        if compliance_standards is None:
            compliance_standards = ["gdpr", "ccpa", "coppa", "hipaa"]

        # Analyze consent clarity and completeness
        consent_analysis = await self._analyze_consent_clarity(consent_text, judge_model)

        # Check compliance with each standard
        compliance_results = {}
        for standard in compliance_standards:
            compliance = await self._check_standard_compliance(consent_text, data_practices, standard, judge_model)
            compliance_results[standard] = compliance

        # Identify consent gaps
        consent_gaps = self._identify_consent_gaps(consent_text, data_practices)

        # Calculate overall compliance score
        compliance_scores = [result["compliance_score"] for result in compliance_results.values()]
        overall_compliance = statistics.mean(compliance_scores) if compliance_scores else 0.0

        return {
            "overall_compliance": overall_compliance,
            "consent_analysis": consent_analysis,
            "compliance_results": compliance_results,
            "consent_gaps": consent_gaps,
            "analysis": {
                "compliance_standards": compliance_standards,
                "consent_clarity_score": consent_analysis.get("clarity_score", 0.0),
                "weakest_compliance": min(compliance_results.items(), key=lambda x: x[1]["compliance_score"])[0] if compliance_results else None,
                "strongest_compliance": max(compliance_results.items(), key=lambda x: x[1]["compliance_score"])[0] if compliance_results else None,
            },
            "recommendations": self._generate_compliance_recommendations(overall_compliance, consent_gaps, compliance_results),
        }

    async def measure_anonymization_effectiveness(
        self,
        original_data: str,
        anonymized_data: str,
        anonymization_method: str = "unknown",
        reidentification_risk_threshold: float = 0.1,
        judge_model: str = "gpt-4o-mini",
    ) -> Dict[str, Any]:
        """Evaluate effectiveness of data anonymization techniques.

        Args:
            original_data: Original data before anonymization
            anonymized_data: Data after anonymization
            anonymization_method: Method used for anonymization
            reidentification_risk_threshold: Acceptable re-identification risk level
            judge_model: Judge model for anonymization assessment

        Returns:
            Anonymization effectiveness analysis
        """
        # Analyze information loss
        information_loss = self._calculate_information_loss(original_data, anonymized_data)

        # Assess re-identification risk
        reidentification_risk = await self._assess_reidentification_risk(original_data, anonymized_data, judge_model)

        # Check for quasi-identifiers
        quasi_identifiers = self._detect_quasi_identifiers(anonymized_data)

        # Evaluate anonymization quality
        anonymization_quality = await self._evaluate_anonymization_quality(original_data, anonymized_data, anonymization_method, judge_model)

        # Calculate effectiveness score
        # Good anonymization should have low re-identification risk but preserve utility
        risk_score = 1.0 - reidentification_risk["risk_score"]
        utility_score = 1.0 - information_loss["loss_ratio"]
        effectiveness_score = (risk_score * 0.6) + (utility_score * 0.4)  # Weight privacy higher

        # Check if meets threshold
        meets_threshold = reidentification_risk["risk_score"] <= reidentification_risk_threshold

        return {
            "effectiveness_score": effectiveness_score,
            "meets_threshold": meets_threshold,
            "information_loss": information_loss,
            "reidentification_risk": reidentification_risk,
            "quasi_identifiers": quasi_identifiers,
            "anonymization_quality": anonymization_quality,
            "analysis": {
                "anonymization_method": anonymization_method,
                "risk_threshold": reidentification_risk_threshold,
                "data_utility_preserved": utility_score,
                "privacy_protection_level": risk_score,
                "quasi_identifier_count": len(quasi_identifiers),
            },
            "recommendations": self._generate_anonymization_recommendations(effectiveness_score, reidentification_risk, information_loss),
        }

    async def detect_data_leakage(
        self,
        input_data: str,
        output_data: str,
        expected_data_flow: Dict[str, Any] = None,
        leakage_types: List[str] = None,
        judge_model: str = "gpt-4o-mini",
    ) -> Dict[str, Any]:
        """Identify unintended data exposure or leakage.

        Args:
            input_data: Input data provided to system
            output_data: Output data generated by system
            expected_data_flow: Expected data transformation rules
            leakage_types: Types of data leakage to check for
            judge_model: Judge model for leakage assessment

        Returns:
            Data leakage analysis
        """
        if leakage_types is None:
            leakage_types = ["direct_exposure", "inference_leakage", "aggregation_leakage", "temporal_leakage"]

        # Detect direct data exposure
        direct_leakage = self._detect_direct_leakage(input_data, output_data)

        # Check for inference-based leakage
        inference_leakage = await self._detect_inference_leakage(input_data, output_data, judge_model)

        # Analyze unexpected data inclusion
        unexpected_data = self._identify_unexpected_data(input_data, output_data, expected_data_flow)

        # LLM assessment of data leakage
        llm_leakage_assessment = await self._llm_assess_data_leakage(input_data, output_data, leakage_types, judge_model)

        # Calculate leakage severity
        leakage_indicators = len(direct_leakage) + len(inference_leakage) + len(unexpected_data)

        # Normalize by text length
        text_length_factor = max(1, len(output_data.split()) / 100)
        leakage_score = leakage_indicators / text_length_factor
        leakage_score = min(1.0, leakage_score)

        # Determine severity
        if leakage_score >= 0.7:
            severity = "critical"
        elif leakage_score >= 0.4:
            severity = "high"
        elif leakage_score >= 0.2:
            severity = "medium"
        else:
            severity = "low"

        return {
            "leakage_score": leakage_score,
            "severity": severity,
            "direct_leakage": direct_leakage,
            "inference_leakage": inference_leakage,
            "unexpected_data": unexpected_data,
            "llm_assessment": llm_leakage_assessment,
            "analysis": {
                "leakage_types_checked": leakage_types,
                "total_leakage_indicators": leakage_indicators,
                "input_length": len(input_data),
                "output_length": len(output_data),
                "expected_data_flow": expected_data_flow is not None,
            },
            "recommendations": self._generate_leakage_recommendations(leakage_score, direct_leakage, inference_leakage),
        }

    async def assess_consent_clarity(
        self,
        consent_text: str,
        target_audience: str = "general_public",
        clarity_dimensions: List[str] = None,
        judge_model: str = "gpt-4o-mini",
    ) -> Dict[str, Any]:
        """Evaluate clarity and comprehensibility of consent notices.

        Args:
            consent_text: Consent notice or privacy policy text
            target_audience: Target audience for the consent notice
            clarity_dimensions: Aspects of clarity to evaluate
            judge_model: Judge model for clarity assessment

        Returns:
            Consent clarity analysis
        """
        if clarity_dimensions is None:
            clarity_dimensions = ["readability", "completeness", "specificity", "accessibility", "actionability"]

        # Analyze readability metrics
        readability_analysis = self._analyze_consent_readability(consent_text)

        # Assess each clarity dimension
        dimension_scores = {}
        for dimension in clarity_dimensions:
            score = await self._assess_clarity_dimension(consent_text, dimension, target_audience, judge_model)
            dimension_scores[dimension] = score

        # Check for required elements
        required_elements = self._check_required_consent_elements(consent_text)

        # Identify clarity issues
        clarity_issues = self._identify_clarity_issues(consent_text, target_audience)

        # Calculate overall clarity score
        overall_clarity = statistics.mean(dimension_scores.values()) if dimension_scores else 0.0

        # Adjust for readability
        readability_factor = readability_analysis.get("accessibility_score", 0.5)
        adjusted_clarity = (overall_clarity * 0.7) + (readability_factor * 0.3)

        return {
            "overall_clarity": adjusted_clarity,
            "dimension_scores": dimension_scores,
            "readability_analysis": readability_analysis,
            "required_elements": required_elements,
            "clarity_issues": clarity_issues,
            "analysis": {
                "target_audience": target_audience,
                "clarity_dimensions": clarity_dimensions,
                "text_length": len(consent_text),
                "issues_identified": len(clarity_issues),
                "readability_grade": readability_analysis.get("grade_level", "unknown"),
            },
            "recommendations": self._generate_clarity_recommendations(adjusted_clarity, clarity_issues, readability_analysis),
        }

    async def evaluate_data_retention_compliance(
        self,
        retention_policies: Dict[str, Any],
        actual_practices: Dict[str, Any],
        regulatory_requirements: List[str] = None,
        judge_model: str = "gpt-4o-mini",
    ) -> Dict[str, Any]:
        """Assess data retention policy compliance and effectiveness.

        Args:
            retention_policies: Stated data retention policies
            actual_practices: Actual data retention practices
            regulatory_requirements: Regulatory standards to check against
            judge_model: Judge model for compliance assessment

        Returns:
            Data retention compliance analysis
        """
        if regulatory_requirements is None:
            regulatory_requirements = ["gdpr_erasure", "ccpa_deletion", "coppa_retention", "sector_specific"]

        # Compare policies vs practices
        policy_practice_alignment = self._compare_policies_practices(retention_policies, actual_practices)

        # Check regulatory compliance
        regulatory_compliance = {}
        for requirement in regulatory_requirements:
            compliance = await self._check_retention_compliance(retention_policies, actual_practices, requirement, judge_model)
            regulatory_compliance[requirement] = compliance

        # Identify retention violations
        retention_violations = self._identify_retention_violations(retention_policies, actual_practices)

        # Calculate compliance score
        compliance_scores = [comp["compliance_score"] for comp in regulatory_compliance.values()]
        overall_compliance = statistics.mean(compliance_scores) if compliance_scores else 0.0

        # Factor in policy-practice alignment
        alignment_score = policy_practice_alignment.get("alignment_score", 0.5)
        adjusted_compliance = (overall_compliance * 0.6) + (alignment_score * 0.4)

        return {
            "overall_compliance": adjusted_compliance,
            "policy_practice_alignment": policy_practice_alignment,
            "regulatory_compliance": regulatory_compliance,
            "retention_violations": retention_violations,
            "analysis": {
                "regulatory_requirements": regulatory_requirements,
                "policies_defined": len(retention_policies),
                "practices_documented": len(actual_practices),
                "violations_found": len(retention_violations),
                "alignment_score": alignment_score,
            },
            "recommendations": self._generate_retention_recommendations(adjusted_compliance, retention_violations, regulatory_compliance),
        }

    async def assess_privacy_by_design(
        self,
        system_description: str,
        privacy_controls: List[Dict[str, Any]],
        design_principles: List[str] = None,
        judge_model: str = "gpt-4o-mini",
    ) -> Dict[str, Any]:
        """Evaluate privacy-by-design implementation in systems.

        Args:
            system_description: Description of the system or process
            privacy_controls: List of implemented privacy controls
            design_principles: Privacy-by-design principles to evaluate
            judge_model: Judge model for privacy assessment

        Returns:
            Privacy-by-design analysis
        """
        if design_principles is None:
            design_principles = ["proactive", "privacy_default", "privacy_embedded", "full_functionality", "end_to_end_security", "visibility_transparency", "user_privacy"]

        # Assess each design principle
        principle_assessments = {}
        for principle in design_principles:
            assessment = await self._assess_design_principle(system_description, privacy_controls, principle, judge_model)
            principle_assessments[principle] = assessment

        # Evaluate privacy controls effectiveness
        controls_effectiveness = self._evaluate_privacy_controls(privacy_controls)

        # Check for privacy gaps
        privacy_gaps = await self._identify_privacy_gaps(system_description, privacy_controls, judge_model)

        # Calculate overall privacy-by-design score
        principle_scores = [assessment["score"] for assessment in principle_assessments.values()]
        controls_score = controls_effectiveness.get("overall_effectiveness", 0.0)

        # Weight: 60% principles, 40% controls implementation
        overall_privacy_design = (statistics.mean(principle_scores) * 0.6 + controls_score * 0.4) if principle_scores else 0.0

        return {
            "overall_privacy_design": overall_privacy_design,
            "principle_assessments": principle_assessments,
            "controls_effectiveness": controls_effectiveness,
            "privacy_gaps": privacy_gaps,
            "analysis": {
                "design_principles": design_principles,
                "controls_implemented": len(privacy_controls),
                "gaps_identified": len(privacy_gaps),
                "weakest_principle": min(principle_assessments.items(), key=lambda x: x[1]["score"])[0] if principle_assessments else None,
                "strongest_principle": max(principle_assessments.items(), key=lambda x: x[1]["score"])[0] if principle_assessments else None,
            },
            "recommendations": self._generate_privacy_design_recommendations(overall_privacy_design, privacy_gaps, principle_assessments),
        }

    # Helper methods for privacy evaluation

    def _calculate_pii_confidence(self, value: str, pii_type: str) -> float:
        """Calculate confidence that detected value is actually PII.

        Args:
            value: The detected PII value
            pii_type: The type of PII detected

        Returns:
            Confidence score between 0 and 1
        """
        # Simple confidence scoring based on pattern strength
        confidence_scores = {
            "email": 0.9 if "@" in value and "." in value else 0.3,
            "phone": 0.8 if len(re.sub(r"[^0-9]", "", value)) == 10 else 0.4,
            "ssn": 0.9 if len(re.sub(r"[^0-9]", "", value)) == 9 else 0.3,
            "credit_card": 0.85,  # Pattern is quite specific
            "ip_address": 0.7,
            "address": 0.6,  # Street address patterns can be noisy
            "name": 0.4,  # Name detection is least reliable
        }

        return confidence_scores.get(pii_type, 0.5)

    def _assess_pii_risk_level(self, pii_type: str) -> str:
        """Assess risk level of different PII types.

        Args:
            pii_type: Type of PII to assess

        Returns:
            Risk level ('high', 'medium', 'low')
        """
        high_risk = ["ssn", "credit_card", "bank_account", "medical_record"]
        medium_risk = ["email", "phone", "address", "name"]

        if pii_type in high_risk:
            return "high"
        if pii_type in medium_risk:
            return "medium"
        return "low"

    async def _llm_pii_assessment(self, text: str, pii_types: List[str], judge_model: str) -> Dict[str, Any]:
        """Use LLM to assess PII presence.

        Args:
            text: Text to analyze for PII
            pii_types: Types of PII to look for
            judge_model: Judge model for assessment

        Returns:
            LLM assessment results with PII score and reasoning
        """
        criteria = [
            {
                "name": "pii_detection",
                "description": "Does the text contain personally identifiable information?",
                "scale": "1-5",
                "weight": 1.0,
            }
        ]

        try:
            result = await self.judge_tools.evaluate_response(
                response=text,
                criteria=criteria,
                rubric={
                    "criteria": criteria,
                    "scale_description": {
                        "1": "No PII detected",
                        "2": "Minimal PII indicators",
                        "3": "Some PII present",
                        "4": "Clear PII exposure",
                        "5": "Extensive PII exposure",
                    },
                },
                judge_model=judge_model,
                context=f"Analyzing for PII types: {', '.join(pii_types)}",
            )

            return {
                "pii_score": (result["overall_score"] - 1) / 4,
                "reasoning": result.get("reasoning", {}),
            }
        except Exception:
            return {"pii_score": 0.0, "reasoning": {"error": "Assessment failed"}}

    def _categorize_collected_data(self, data: Dict[str, Any], categories: List[str]) -> Dict[str, Any]:
        """Categorize collected data by privacy categories.

        Args:
            data: Data to categorize
            categories: Privacy categories to use

        Returns:
            Categorized data mapping
        """
        categorization = {category: [] for category in categories}

        # Simple categorization based on field names and values
        for field, value in data.items():
            field_lower = field.lower()

            if any(identifier in field_lower for identifier in ["name", "email", "phone", "address"]):
                categorization["personal_identifiers"].append({"field": field, "value": str(value)[:50]})
            elif any(financial in field_lower for financial in ["card", "payment", "account", "bank"]):
                categorization["financial"].append({"field": field, "value": str(value)[:50]})
            elif any(medical in field_lower for medical in ["health", "medical", "diagnosis", "medication"]):
                categorization["medical"].append({"field": field, "value": str(value)[:50]})
            elif any(behavioral in field_lower for behavioral in ["history", "preference", "activity", "behavior"]):
                categorization["behavioral"].append({"field": field, "value": str(value)[:50]})
            elif any(sensitive in field_lower for sensitive in ["race", "religion", "political", "orientation"]):
                categorization["sensitive_attributes"].append({"field": field, "value": str(value)[:50]})

        return categorization

    def _calculate_information_loss(self, original: str, anonymized: str) -> Dict[str, Any]:
        """Calculate information loss due to anonymization.

        Args:
            original: Original text before anonymization
            anonymized: Text after anonymization

        Returns:
            Information loss metrics
        """
        original_words = set(original.lower().split())
        anonymized_words = set(anonymized.lower().split())

        if not original_words:
            return {"loss_ratio": 0.0, "preserved_ratio": 1.0}

        preserved_words = original_words & anonymized_words
        lost_words = original_words - anonymized_words

        loss_ratio = len(lost_words) / len(original_words)
        preserved_ratio = len(preserved_words) / len(original_words)

        return {
            "loss_ratio": loss_ratio,
            "preserved_ratio": preserved_ratio,
            "words_lost": len(lost_words),
            "words_preserved": len(preserved_words),
            "total_original_words": len(original_words),
        }

    # Placeholder implementations for complex methods
    async def _assess_data_necessity(self, data_cat: Dict, _purpose: str, _judge_model: str) -> List[Dict[str, Any]]:
        """Assess if collected data is necessary for stated purpose.

        Args:
            data_cat: Categorized data to assess
            _purpose: Stated purpose for data collection
            _judge_model: Judge model for assessment

        Returns:
            List of necessity assessments for each data item
        """
        assessments = []
        for category, items in data_cat.items():
            for item in items:
                assessments.append(
                    {
                        "data_field": item["field"],
                        "category": category,
                        "necessary": True,  # Simplified
                        "necessity_score": 0.8,
                    }
                )
        return assessments

    async def _analyze_purpose_alignment(self, _data: Dict, _purpose: str, _judge_model: str) -> Dict[str, Any]:
        """Analyze alignment between data collection and stated purpose.

        Args:
            _data: Collected data to analyze
            _purpose: Stated purpose for data collection
            _judge_model: Judge model for analysis

        Returns:
            Purpose alignment analysis results
        """
        return {"alignment_score": 0.8, "misaligned_fields": []}

    async def _analyze_consent_clarity(self, _consent_text: str, _judge_model: str) -> Dict[str, Any]:
        """Analyze consent notice clarity.

        Args:
            _consent_text: Consent notice text to analyze
            _judge_model: Judge model for clarity analysis

        Returns:
            Consent clarity analysis results
        """
        return {"clarity_score": 0.7, "readability_grade": "college"}

    async def _check_standard_compliance(self, _consent: str, _practices: Dict, _standard: str, _judge_model: str) -> Dict[str, Any]:
        """Check compliance with specific privacy standard.

        Args:
            _consent: Consent text to check
            _practices: Actual data practices
            _standard: Privacy standard to check against
            _judge_model: Judge model for compliance assessment

        Returns:
            Compliance assessment results
        """
        return {"compliance_score": 0.8, "violations": [], "requirements_met": []}

    def _identify_consent_gaps(self, _consent_text: str, _practices: Dict) -> List[Dict[str, Any]]:
        """Identify gaps between consent and actual practices.

        Args:
            _consent_text: Consent notice text
            _practices: Actual data practices

        Returns:
            List of identified gaps
        """
        return []  # Simplified

    async def _assess_reidentification_risk(self, _original: str, _anonymized: str, _judge_model: str) -> Dict[str, Any]:
        """Assess risk of re-identification.

        Args:
            _original: Original data before anonymization
            _anonymized: Anonymized data
            _judge_model: Judge model for risk assessment

        Returns:
            Re-identification risk assessment
        """
        return {"risk_score": 0.2, "risk_factors": [], "confidence": 0.8}

    def _detect_quasi_identifiers(self, _data: str) -> List[Dict[str, Any]]:
        """Detect quasi-identifiers that could enable re-identification.

        Args:
            _data: Data to analyze for quasi-identifiers

        Returns:
            List of detected quasi-identifiers
        """
        return []  # Simplified

    # Recommendation generation methods
    def _generate_pii_recommendations(self, risk_score: float, detected_pii: List) -> List[str]:
        """Generate recommendations for PII protection.

        Args:
            risk_score: PII risk score
            detected_pii: List of detected PII items

        Returns:
            List of recommendations
        """
        recommendations = []

        if risk_score > 0.7:
            recommendations.append("High PII exposure detected - implement data masking or removal")
        elif risk_score > 0.4:
            recommendations.append("Moderate PII risk - review and minimize data exposure")

        if detected_pii:
            pii_types = list(set(item["type"] for item in detected_pii))
            recommendations.append(f"Address exposure of: {', '.join(pii_types)}")

        return recommendations

    def _generate_minimization_recommendations(self, score: float, excessive_data: List) -> List[str]:
        """Generate recommendations for data minimization.

        Args:
            score: Minimization score
            excessive_data: List of excessive data items

        Returns:
            List of recommendations
        """
        recommendations = []

        if score < 0.8:
            recommendations.append("Implement stricter data minimization practices")

        if excessive_data:
            excessive_types = [data["data_field"] for data in excessive_data[:3]]
            recommendations.append(f"Remove or justify collection of: {', '.join(excessive_types)}")

        return recommendations

    def _generate_compliance_recommendations(self, compliance: float, gaps: List, results: Dict) -> List[str]:
        """Generate recommendations for compliance improvement.

        Args:
            compliance: Overall compliance score
            gaps: List of compliance gaps
            results: Detailed compliance results

        Returns:
            List of recommendations
        """
        recommendations = []

        if compliance < 0.8:
            recommendations.append("Improve overall privacy compliance")

        if gaps:
            recommendations.append("Address identified consent and practice gaps")

        low_compliance = [standard for standard, data in results.items() if data["compliance_score"] < 0.7]
        if low_compliance:
            recommendations.append(f"Focus on compliance with: {', '.join(low_compliance)}")

        return recommendations

    def _generate_anonymization_recommendations(self, effectiveness: float, risk: Dict, loss: Dict) -> List[str]:
        """Generate recommendations for anonymization improvement.

        Args:
            effectiveness: Anonymization effectiveness score
            risk: Re-identification risk assessment
            loss: Information loss assessment

        Returns:
            List of recommendations
        """
        recommendations = []

        if effectiveness < 0.7:
            recommendations.append("Improve anonymization effectiveness")

        if risk["risk_score"] > 0.2:
            recommendations.append("Reduce re-identification risk through stronger anonymization")

        if loss["loss_ratio"] > 0.8:
            recommendations.append("Consider utility-preserving anonymization techniques")

        return recommendations

    def _generate_leakage_recommendations(self, score: float, direct: List, inference: List) -> List[str]:
        """Generate recommendations for preventing data leakage.

        Args:
            score: Data leakage score
            direct: List of direct leakage instances
            inference: List of inference leakage instances

        Returns:
            List of recommendations
        """
        recommendations = []

        if score > 0.4:
            recommendations.append("Implement stronger data leakage prevention")

        if direct:
            recommendations.append("Address direct data exposure in outputs")

        if inference:
            recommendations.append("Mitigate inference-based data leakage")

        return recommendations

    def _generate_clarity_recommendations(self, clarity: float, issues: List, readability: Dict) -> List[str]:
        """Generate recommendations for consent clarity.

        Args:
            clarity: Overall clarity score
            issues: List of clarity issues
            readability: Readability analysis results

        Returns:
            List of recommendations
        """
        recommendations = []

        if clarity < 0.7:
            recommendations.append("Improve consent notice clarity and comprehensibility")

        if readability.get("grade_level", 12) > 10:
            recommendations.append("Simplify language for better accessibility")

        if issues:
            recommendations.append("Address identified clarity and comprehension issues")

        return recommendations

    def _generate_privacy_design_recommendations(self, score: float, gaps: List, assessments: Dict) -> List[str]:
        """Generate recommendations for privacy-by-design improvement.

        Args:
            score: Overall privacy design score
            gaps: List of privacy gaps
            assessments: Principle assessments

        Returns:
            List of recommendations
        """
        recommendations = []

        if score < 0.8:
            recommendations.append("Strengthen privacy-by-design implementation")

        if gaps:
            recommendations.append("Address identified privacy design gaps")

        weak_principles = [principle for principle, data in assessments.items() if data["score"] < 0.6]
        if weak_principles:
            recommendations.append(f"Improve implementation of: {', '.join(weak_principles)}")

        return recommendations

    def _generate_retention_recommendations(self, compliance: float, violations: List, regulatory_compliance: Dict) -> List[str]:
        """Generate recommendations for data retention compliance.

        Args:
            compliance: Overall compliance score
            violations: List of retention violations
            regulatory_compliance: Regulatory compliance results

        Returns:
            List of recommendations
        """
        recommendations = []

        if compliance < 0.8:
            recommendations.append("Improve data retention policy compliance")

        if violations:
            recommendations.append("Address identified retention policy violations")

        low_compliance = [standard for standard, data in regulatory_compliance.items() if data["compliance_score"] < 0.7]
        if low_compliance:
            recommendations.append(f"Focus on retention compliance with: {', '.join(low_compliance)}")

        return recommendations

    # Additional placeholder methods for complex operations
    def _detect_direct_leakage(self, _input_data: str, _output_data: str) -> List[Dict[str, Any]]:
        """Detect direct data leakage.

        Args:
            _input_data: Input data provided to system
            _output_data: Output data generated by system

        Returns:
            List of direct leakage instances
        """
        return []

    async def _detect_inference_leakage(self, _input_data: str, _output_data: str, _judge_model: str) -> List[Dict[str, Any]]:
        """Detect inference-based leakage.

        Args:
            _input_data: Input data provided to system
            _output_data: Output data generated by system
            _judge_model: Judge model for inference analysis

        Returns:
            List of inference leakage instances
        """
        return []

    def _identify_unexpected_data(self, _input_data: str, _output_data: str, _expected_flow: Optional[Dict]) -> List[Dict[str, Any]]:
        """Identify unexpected data in outputs.

        Args:
            _input_data: Input data provided to system
            _output_data: Output data generated by system
            _expected_flow: Expected data flow rules

        Returns:
            List of unexpected data instances
        """
        return []

    async def _llm_assess_data_leakage(self, _input_data: str, _output_data: str, _types: List[str], _judge_model: str) -> Dict[str, Any]:
        """LLM assessment of data leakage.

        Args:
            _input_data: Input data provided to system
            _output_data: Output data generated by system
            _types: Types of leakage to check for
            _judge_model: Judge model for leakage assessment

        Returns:
            LLM-based leakage assessment results
        """
        return {"leakage_detected": False, "confidence": 0.8}

    def _analyze_consent_readability(self, text: str) -> Dict[str, Any]:
        """Analyze readability of consent text.

        Args:
            text: Consent text to analyze

        Returns:
            Readability analysis results
        """
        # Simple readability analysis
        words = len(text.split())
        sentences = len(re.split(r"[.!?]+", text))
        avg_words_per_sentence = words / max(1, sentences)

        # Estimate reading grade level (simplified)
        grade_level = min(16, max(6, avg_words_per_sentence - 5))

        return {
            "word_count": words,
            "sentence_count": sentences,
            "avg_words_per_sentence": avg_words_per_sentence,
            "grade_level": grade_level,
            "accessibility_score": max(0.0, 1.0 - (grade_level - 8) / 8),  # Lower grade = more accessible
        }

    async def _assess_clarity_dimension(self, _text: str, _dimension: str, _audience: str, _judge_model: str) -> float:
        """Assess specific clarity dimension.

        Args:
            _text: Text to assess
            _dimension: Clarity dimension to evaluate
            _audience: Target audience
            _judge_model: Judge model for assessment

        Returns:
            Clarity dimension score
        """
        return 0.7  # Placeholder

    def _check_required_consent_elements(self, consent_text: str) -> Dict[str, bool]:
        """Check for required consent elements.

        Args:
            consent_text: Consent text to check

        Returns:
            Mapping of required elements to presence status
        """
        elements = {
            "purpose_statement": "purpose" in consent_text.lower(),
            "data_types": any(word in consent_text.lower() for word in ["data", "information", "personal"]),
            "retention_period": any(word in consent_text.lower() for word in ["retain", "keep", "store", "delete"]),
            "user_rights": any(word in consent_text.lower() for word in ["right", "access", "delete", "opt-out"]),
            "contact_info": "@" in consent_text or "contact" in consent_text.lower(),
        }

        return elements

    def _identify_clarity_issues(self, text: str, _audience: str) -> List[Dict[str, Any]]:
        """Identify clarity issues in consent text.

        Args:
            text: Consent text to analyze
            _audience: Target audience

        Returns:
            List of identified clarity issues
        """
        issues = []

        # Check for overly complex language
        words = text.split()
        long_words = [word for word in words if len(word) > 12]
        if len(long_words) > len(words) * 0.1:
            issues.append({"type": "complex_language", "severity": "medium", "description": "Too many complex words for target audience"})

        # Check for very long sentences
        sentences = re.split(r"[.!?]+", text)
        long_sentences = [s for s in sentences if len(s.split()) > 30]
        if len(long_sentences) > len(sentences) * 0.2:
            issues.append({"type": "long_sentences", "severity": "medium", "description": "Sentences are too long for easy comprehension"})

        return issues

    def _compare_policies_practices(self, _policies: Dict, _practices: Dict) -> Dict[str, Any]:
        """Compare stated policies with actual practices.

        Args:
            _policies: Stated retention policies
            _practices: Actual retention practices

        Returns:
            Policy-practice alignment analysis
        """
        return {"alignment_score": 0.8, "discrepancies": []}

    def _identify_retention_violations(self, _policies: Dict, _practices: Dict) -> List[Dict[str, Any]]:
        """Identify retention policy violations.

        Args:
            _policies: Stated retention policies
            _practices: Actual retention practices

        Returns:
            List of identified violations
        """
        return []

    async def _check_retention_compliance(self, _policies: Dict, _practices: Dict, _requirement: str, _judge_model: str) -> Dict[str, Any]:
        """Check retention compliance with specific requirement.

        Args:
            _policies: Stated retention policies
            _practices: Actual retention practices
            _requirement: Specific regulatory requirement
            _judge_model: Judge model for compliance assessment

        Returns:
            Compliance assessment results
        """
        return {"compliance_score": 0.8, "violations": []}

    def _evaluate_privacy_controls(self, controls: List[Dict[str, Any]]) -> Dict[str, Any]:
        """Evaluate effectiveness of privacy controls.

        Args:
            controls: List of privacy controls to evaluate

        Returns:
            Privacy controls effectiveness analysis
        """
        if not controls:
            return {"overall_effectiveness": 0.0}

        # Simple effectiveness scoring
        control_scores = []
        for control in controls:
            # Score based on control type and implementation
            control.get("type", "unknown")
            implementation_score = control.get("implementation_score", 0.5)
            control_scores.append(implementation_score)

        return {
            "overall_effectiveness": statistics.mean(control_scores) if control_scores else 0.0,
            "controls_evaluated": len(controls),
            "control_scores": control_scores,
        }

    async def _assess_design_principle(self, _description: str, _controls: List, _principle: str, _judge_model: str) -> Dict[str, Any]:
        """Assess implementation of privacy-by-design principle.

        Args:
            _description: System description
            _controls: List of privacy controls
            _principle: Privacy-by-design principle to assess
            _judge_model: Judge model for assessment

        Returns:
            Principle implementation assessment
        """
        return {"score": 0.7, "evidence": [], "gaps": []}

    async def _identify_privacy_gaps(self, _description: str, _controls: List, _judge_model: str) -> List[Dict[str, Any]]:
        """Identify privacy implementation gaps.

        Args:
            _description: System description
            _controls: List of privacy controls
            _judge_model: Judge model for gap analysis

        Returns:
            List of identified privacy gaps
        """
        return []

    async def _evaluate_anonymization_quality(self, _original: str, _anonymized: str, _method: str, _judge_model: str) -> Dict[str, Any]:
        """Evaluate quality of anonymization.

        Args:
            _original: Original data before anonymization
            _anonymized: Data after anonymization
            _method: Anonymization method used
            _judge_model: Judge model for quality assessment

        Returns:
            Anonymization quality assessment
        """
        return {"quality_score": 0.8, "method_effectiveness": _method}
