# patent_8_privacy_validation.py import hashlib import random import logging import numpy as np # For numerical operations, especially for confidence scores # Configure logging (important for auditing and debugging) logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') class PrivacyPreservingValidator: """ Validates AI model outputs without exposing sensitive data, using data masking, tokenization, differential privacy, and secure confidence scoring. """ def __init__(self, epsilon=1.0, delta=1e-5): # Differential privacy parameters """ Initializes the validator. Args: epsilon (float): Privacy parameter for differential privacy. Lower values mean more privacy. delta (float): Privacy parameter for differential privacy. Represents the probability of a privacy breach. """ self.epsilon = epsilon self.delta = delta self.tokenizer = self.create_tokenizer() # Initialize tokenizer self.salt = self.generate_salt() # Add a salt for hashing to prevent rainbow table attacks def create_tokenizer(self): """ Creates a simple tokenizer (replace with a more sophisticated one if needed). For this example, it's a dictionary mapping values to tokens. In real applications, consider using libraries like scikit-learn's CountVectorizer or more advanced NLP tokenizers. """ # Example token vocabulary (expand as needed) vocabulary = { "positive": 1, "negative": 2, "neutral": 3, "true": 4, "false": 5, "high": 6, "low": 7, "medium": 8, } return vocabulary def generate_salt(self): """ Generates a random salt for hashing. This is crucial for security. """ return str(random.randint(100000, 999999)) # Generate a random 6-digit number as a string def mask_data(self, data): """ Masks sensitive data using techniques like generalization or suppression. This is a placeholder; implement domain-specific masking logic here. Args: data (dict): The data to be masked (e.g., a dictionary of key-value pairs). Returns: dict: The masked data. """ masked_data = {} for key, value in data.items(): # Example: Replace email addresses with a generic placeholder if "email" in key.lower(): masked_data[key] = "[EMAIL_REDACTED]" # Example: Replace numerical values with a range elif isinstance(value, (int, float)): masked_data[key] = "[NUMERICAL_VALUE]" # Or a range, e.g., "[0-100]" else: masked_data[key] = value # Keep other values as they are (for now) logging.info(f"Data masked: {masked_data}") # Log the masked data (without sensitive parts!) return masked_data def tokenize_data(self, data): """ Tokenizes the data using the defined tokenizer. Args: data (dict): The data to be tokenized. Returns: dict: The tokenized data. """ tokenized_data = {} for key, value in data.items(): if isinstance(value, str): try: tokenized_data[key] = self.tokenizer[value.lower()] # Convert to lowercase for consistency except KeyError: # Handle unknown tokens (e.g., assign a special "unknown" token) tokenized_data[key] = 0 # Or raise an error, depending on your requirements logging.warning(f"Unknown token: {value}") # Log unknown tokens else: tokenized_data[key] = value # Keep non-string values as they are logging.info(f"Data tokenized: {tokenized_data}") return tokenized_data def hash_data(self, data): """ Hashes the data using SHA-256. Includes a salt to prevent rainbow table attacks. Args: data (dict): The data to be hashed. Returns: str: The hexadecimal representation of the hash. """ data_str = str(data) + self.salt # Combine data with salt hashed_data = hashlib.sha256(data_str.encode('utf-8')).hexdigest() logging.debug(f"Data hashed (first 10 characters): {hashed_data[:10]}...") # Log a truncated hash for debugging return hashed_data def privacy_safe_confidence_score(self, confidence_score): """ Calculates a privacy-safe confidence score using differential privacy. Args: confidence_score (float): The original confidence score (between 0 and 1). Returns: float: The differentially private confidence score. """ # Implement Laplace mechanism for differential privacy sensitivity = 1.0 # Sensitivity of the confidence score (max change) beta = sensitivity / self.epsilon # Parameter for Laplace distribution # Add Laplace noise noise = np.random.laplace(0, beta) private_score = confidence_score + noise # Clip the score to [0, 1] private_score = np.clip(private_score, 0, 1) logging.info(f"Original score: {confidence_score}, Private score: {private_score}") return private_score def validate_output(self, model_output, expected_output): """ Validates the model output against the expected output in a privacy-preserving manner. Args: model_output (dict): The output from the AI model. expected_output (dict): The expected output for validation. Returns: bool: True if the validation is successful (based on hashed data), False otherwise. """ # 1. Mask sensitive data masked_model_output = self.mask_data(model_output) masked_expected_output = self.mask_data(expected_output) # 2. Tokenize the masked data tokenized_model_output = self.tokenize_data(masked_model_output) tokenized_expected_output = self.tokenize_data(masked_expected_output) # 3. Hash the tokenized data hashed_model_output = self.hash_data(tokenized_model_output) hashed_expected_output = self.hash_data(tokenized_expected_output) # 4. Compare the hashes validation_result = hashed_model_output == hashed_expected_output logging.info(f"Validation result: {validation_result}") return validation_result # Example usage: if __name__ == "__main__": validator = PrivacyPreservingValidator(epsilon=0.5) # Example model output and expected output (containing sensitive data) model_output = { "prediction": "positive", "confidence": 0.85, "user_email": "test@example.com", "age": 30, } expected_output = { "prediction": "positive", "confidence": 0.90, "user_email": "test@example.com", # Ideally, expected output would also be masked "age": 30, } # Validate the output is_valid = validator.validate_output(model_output, expected_output) print(f"Is validation successful? {is_valid}") # Example of privacy-safe confidence scoring original_confidence = model_output["confidence"] private_confidence = validator.privacy_safe_confidence_score(original_confidence) print(f"Original confidence: {original_confidence}, Private confidence: {private_confidence}")