Add implementation summary for privacy risk assessment module

Co-authored-by: mitchelllisle <18128531+mitchelllisle@users.noreply.github.com>

Author: Copilot

PRIVACY_RISK_ASSESSMENT_SUMMARY.md

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+# Privacy Risk Assessment Module - Implementation Summary
+
+## Overview
+This implementation adds a comprehensive privacy risk assessment module to Maskala that evaluates re-identification risks in Spark datasets.
+
+## What Was Implemented
+
+### 1. Core Components
+
+#### TCloseness Analyser (`TCloseness.scala`)
+- Implements t-closeness privacy principle
+- Measures distribution distance using Total Variation Distance
+- Provides methods:
+  - `apply()`: Calculates distribution distances for equivalence classes
+  - `isTClose()`: Checks if dataset satisfies t-closeness
+  - `removeLessThanTRows()`: Filters out non-compliant equivalence classes
+
+#### Privacy Risk Assessment Module (`PrivacyRiskAssessment.scala`)
+- Comprehensive privacy risk evaluation framework
+- Key features:
+  - **Automatic Quasi-Identifier Detection**: Uses heuristics based on column names and cardinality
+  - **Multi-Metric Analysis**: Calculates k-anonymity, l-diversity, and t-closeness simultaneously
+  - **Risk Scoring**: Generates overall risk score (0-100) based on all metrics
+  - **Actionable Recommendations**: Provides specific guidance for improving privacy
+
+##### Main Components:
+- `RiskAssessmentResult`: Case class holding assessment results
+- `PrivacyRiskParams`: Case class for configuration parameters
+- `assess()`: Main method to perform comprehensive risk assessment
+- `detectQuasiIdentifiers()`: Automatic quasi-identifier detection
+- `generateReport()`: Creates formatted risk assessment report
+
+### 2. Testing
+
+#### TClosenessTest (5 tests)
+- Tests for distribution closeness validation
+- Tests for filtering non-compliant records
+- Tests with multiple quasi-identifiers
+- Tests with uniform distributions
+
+#### PrivacyRiskAssessmentTest (10 tests)
+- Automatic quasi-identifier detection tests
+- Basic privacy risk assessment with k-anonymity
+- Combined assessment with l-diversity
+- Combined assessment with t-closeness
+- Uniqueness risk calculation
+- Report generation
+- Tests without ID column
+- Risk score comparison tests
+- Column exclusion tests
+- Cardinality-based detection tests
+
+### 3. Documentation
+
+#### README.md Updates
+- New "Privacy Risk Assessment" section with:
+  - Feature overview and key capabilities
+  - Basic usage examples
+  - Automatic quasi-identifier detection examples
+  - Result interpretation guide
+  - Integration with anonymization workflow
+- New "T-Closeness" section with:
+  - Concept explanation
+  - Usage examples
+  - Filtering examples
+
+#### Example Code (`PrivacyRiskAssessmentExample.scala`)
+- Three comprehensive examples:
+  1. Basic privacy risk assessment
+  2. Automatic quasi-identifier detection
+  3. Before/after anonymization comparison
+
+## Key Features Delivered
+
+1. ✅ **Detects quasi-identifiers** - Automatic detection based on column names and cardinality
+2. ✅ **Calculates k-anonymity** - Minimum group size in dataset
+3. ✅ **Calculates l-diversity** - Diversity of sensitive attributes
+4. ✅ **Calculates t-closeness** - Distribution distance from overall population
+5. ✅ **Generates risk scores** - 0-100 overall risk score with component breakdown
+6. ✅ **Provides recommendations** - Actionable guidance for improving privacy
+7. ✅ **Seamless Spark integration** - Works naturally with DataFrames
+8. ✅ **Comprehensive documentation** - Examples and usage guidance in README
+
+## Privacy Metrics Explained
+
+### K-Anonymity Score
+- Represents the minimum group size in the dataset
+- Higher values indicate better privacy (harder to single out individuals)
+- Contributes up to 40 points to overall risk score
+
+### L-Diversity Score
+- Minimum number of distinct sensitive values in equivalence classes
+- Higher values indicate better diversity
+- Contributes up to 25 points to overall risk score
+
+### T-Closeness Score
+- Maximum distribution distance from overall population
+- Lower values indicate better privacy (distributions are similar)
+- Contributes up to 20 points to overall risk score
+
+### Uniqueness Risk
+- Ratio of records with uniqueness = 1 (highly identifiable)
+- Lower values indicate better privacy
+- Contributes up to 15 points to overall risk score
+
+### Overall Risk Score
+- Composite score from 0-100
+- 0-20: Low risk ✓
+- 20-40: Moderate risk ⚠
+- 40-60: High risk ⚠⚠
+- 60-100: Critical risk ⚠⚠⚠
+
+## Usage Example
+
+```scala
+import org.apache.spark.sql.SparkSession
+import org.mitchelllisle.analysers.{PrivacyRiskAssessment, PrivacyRiskParams}
+
+val spark = SparkSession.builder().getOrCreate()
+import spark.implicits._
+
+val data = Seq(
+  ("1", "30", "Male", "12345", "Heart Disease"),
+  ("2", "30", "Male", "12345", "Diabetes")
+  // ... more data
+).toDF("patient_id", "age", "gender", "zipcode", "disease")
+
+val params = PrivacyRiskParams(
+  quasiIdentifiers = Seq("age", "gender", "zipcode"),
+  sensitiveAttribute = Some("disease"),
+  idColumn = Some("patient_id")
+)
+
+val result = PrivacyRiskAssessment.assess(data, params)
+val report = PrivacyRiskAssessment.generateReport(result)
+println(report)
+```
+
+## Testing Summary
+- Total new tests: 15 (5 for TCloseness, 10 for PrivacyRiskAssessment)
+- All tests passing ✓
+- Existing tests still passing ✓
+- Code compiles successfully ✓
+
+## Integration Points
+- Works with existing KAnonymity, LDiversity, and UniquenessAnalyser classes
+- Compatible with Anonymiser workflow for iterative privacy improvement
+- Follows existing code patterns and conventions in the repository