Data Protection

Comprehensive guide to data protection, privacy, and compliance with regulations like GDPR, CCPA, and financial data protection standards.

Overview

Data protection is essential for maintaining user trust and complying with regulatory requirements. This guide covers data classification, encryption, privacy controls, and compliance with major data protection regulations.

Data Classification

Data Categories

Public Data

Description: Information that can be freely shared without restrictions.

Examples:

Market data and prices
Public company information
General platform statistics
Educational content

Protection Level: Basic Retention: Indefinite Access Control: Open

Internal Data

Description: Information used internally by the organization.

Examples:

Internal policies and procedures
Employee information
System configurations
Business metrics

Protection Level: Medium Retention: 7 years Access Control: Internal employees only

Confidential Data

Description: Sensitive business information requiring protection.

Examples:

Trading algorithms
Business strategies
Financial reports
Partnership agreements

Protection Level: High Retention: 10 years Access Control: Authorized personnel only

Restricted Data

Description: Highly sensitive data with strict access controls.

Examples:

Personal identification information (PII)
Financial account details
Trading history
Authentication credentials

Protection Level: Very High Retention: As required by law Access Control: Need-to-know basis

Data Classification Implementation

Classification Engine

class DataClassifier:
    def __init__(self):
        self.classification_rules = {
            'pii_patterns': [
                r'\b\d{3}-\d{2}-\d{4}\b',  # SSN
                r'\b\d{4}-\d{4}-\d{4}-\d{4}\b',  # Credit card
                r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b'  # Email
            ],
            'financial_patterns': [
                r'\$[\d,]+\.?\d*',  # Currency amounts
                r'\b(account|balance|transaction)\b',  # Financial terms
            ],
            'trading_patterns': [
                r'\b(buy|sell|order|position)\b',  # Trading terms
                r'\b(BTC|ETH|USD)\b',  # Trading symbols
            ]
        }

    def classify_data(self, data):
        classification = 'public'
        confidence = 0.0

        # Check for PII
        pii_score = self._check_pii_patterns(data)
        if pii_score > 0.8:
            classification = 'restricted'
            confidence = pii_score

        # Check for financial data
        financial_score = self._check_financial_patterns(data)
        if financial_score > 0.7 and classification != 'restricted':
            classification = 'confidential'
            confidence = financial_score

        # Check for trading data
        trading_score = self._check_trading_patterns(data)
        if trading_score > 0.6 and classification == 'public':
            classification = 'internal'
            confidence = trading_score

        return {
            'classification': classification,
            'confidence': confidence,
            'timestamp': datetime.utcnow().isoformat()
        }

    def _check_pii_patterns(self, data):
        matches = 0
        total_patterns = len(self.classification_rules['pii_patterns'])

        for pattern in self.classification_rules['pii_patterns']:
            if re.search(pattern, str(data), re.IGNORECASE):
                matches += 1

        return matches / total_patterns

    def _check_financial_patterns(self, data):
        matches = 0
        total_patterns = len(self.classification_rules['financial_patterns'])

        for pattern in self.classification_rules['financial_patterns']:
            if re.search(pattern, str(data), re.IGNORECASE):
                matches += 1

        return matches / total_patterns

    def _check_trading_patterns(self, data):
        matches = 0
        total_patterns = len(self.classification_rules['trading_patterns'])

        for pattern in self.classification_rules['trading_patterns']:
            if re.search(pattern, str(data), re.IGNORECASE):
                matches += 1

        return matches / total_patterns

Data Encryption

Encryption at Rest

Database Encryption

from cryptography.fernet import Fernet
from cryptography.hazmat.primitives import hashes
from cryptography.hazmat.primitives.kdf.pbkdf2 import PBKDF2HMAC
import base64

class DatabaseEncryption:
    def __init__(self, master_key):
        self.master_key = master_key
        self.encryption_keys = {}

    def generate_table_key(self, table_name):
        # Generate unique encryption key for each table
        salt = table_name.encode()
        kdf = PBKDF2HMAC(
            algorithm=hashes.SHA256(),
            length=32,
            salt=salt,
            iterations=100000,
        )
        key = base64.urlsafe_b64encode(kdf.derive(self.master_key))
        self.encryption_keys[table_name] = key
        return key

    def encrypt_field(self, table_name, field_name, value):
        if value is None:
            return None

        key = self.encryption_keys.get(table_name)
        if not key:
            key = self.generate_table_key(table_name)

        f = Fernet(key)
        encrypted_value = f.encrypt(str(value).encode())
        return encrypted_value.decode()

    def decrypt_field(self, table_name, field_name, encrypted_value):
        if encrypted_value is None:
            return None

        key = self.encryption_keys.get(table_name)
        if not key:
            raise ValueError(f"No encryption key found for table {table_name}")

        f = Fernet(key)
        decrypted_value = f.decrypt(encrypted_value.encode())
        return decrypted_value.decode()

File System Encryption

import os
from pathlib import Path

class FileSystemEncryption:
    def __init__(self, encryption_key):
        self.encryption_key = encryption_key
        self.fernet = Fernet(encryption_key)

    def encrypt_file(self, file_path, encrypted_path):
        with open(file_path, 'rb') as file:
            file_data = file.read()

        encrypted_data = self.fernet.encrypt(file_data)

        with open(encrypted_path, 'wb') as encrypted_file:
            encrypted_file.write(encrypted_data)

    def decrypt_file(self, encrypted_path, decrypted_path):
        with open(encrypted_path, 'rb') as encrypted_file:
            encrypted_data = encrypted_file.read()

        decrypted_data = self.fernet.decrypt(encrypted_data)

        with open(decrypted_path, 'wb') as decrypted_file:
            decrypted_file.write(decrypted_data)

    def encrypt_directory(self, directory_path):
        for root, dirs, files in os.walk(directory_path):
            for file in files:
                file_path = os.path.join(root, file)
                encrypted_path = file_path + '.encrypted'
                self.encrypt_file(file_path, encrypted_path)
                os.remove(file_path)  # Remove original file

Encryption in Transit

TLS/SSL Implementation

import ssl
import socket
from cryptography import x509
from cryptography.hazmat.backends import default_backend

class TLSManager:
    def __init__(self):
        self.ssl_context = ssl.create_default_context()
        self.certificate_store = {}

    def create_secure_connection(self, hostname, port):
        # Create secure socket connection
        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
        secure_sock = self.ssl_context.wrap_socket(sock, server_hostname=hostname)
        secure_sock.connect((hostname, port))
        return secure_sock

    def verify_certificate(self, certificate_path):
        # Verify SSL certificate
        with open(certificate_path, 'rb') as cert_file:
            cert_data = cert_file.read()

        certificate = x509.load_pem_x509_certificate(cert_data, default_backend())

        # Check certificate validity
        if certificate.not_valid_after < datetime.utcnow():
            raise ValueError("Certificate has expired")

        if certificate.not_valid_before > datetime.utcnow():
            raise ValueError("Certificate is not yet valid")

        return True

    def create_self_signed_certificate(self, common_name):
        # Create self-signed certificate for development
        from cryptography import x509
        from cryptography.x509.oid import NameOID
        from cryptography.hazmat.primitives import hashes
        from cryptography.hazmat.primitives.asymmetric import rsa

        # Generate private key
        private_key = rsa.generate_private_key(
            public_exponent=65537,
            key_size=2048,
            backend=default_backend()
        )

        # Create certificate
        subject = issuer = x509.Name([
            x509.NameAttribute(NameOID.COUNTRY_NAME, "US"),
            x509.NameAttribute(NameOID.STATE_OR_PROVINCE_NAME, "CA"),
            x509.NameAttribute(NameOID.LOCALITY_NAME, "San Francisco"),
            x509.NameAttribute(NameOID.ORGANIZATION_NAME, "Vibe Trading"),
            x509.NameAttribute(NameOID.COMMON_NAME, common_name),
        ])

        cert = x509.CertificateBuilder().subject_name(
            subject
        ).issuer_name(
            issuer
        ).public_key(
            private_key.public_key()
        ).serial_number(
            x509.random_serial_number()
        ).not_valid_before(
            datetime.utcnow()
        ).not_valid_after(
            datetime.utcnow() + timedelta(days=365)
        ).add_extension(
            x509.SubjectAlternativeName([
                x509.DNSName(common_name),
            ]),
            critical=False,
        ).sign(private_key, hashes.SHA256(), default_backend())

        return cert, private_key

Privacy Controls

Data Minimization

Data Collection Limits

class DataMinimization:
    def __init__(self):
        self.collection_rules = {
            'required_fields': ['email', 'password'],
            'optional_fields': ['phone', 'address'],
            'prohibited_fields': ['ssn', 'credit_card', 'bank_account']
        }

    def validate_data_collection(self, collected_data):
        violations = []

        # Check for prohibited fields
        for field in self.collection_rules['prohibited_fields']:
            if field in collected_data:
                violations.append(f"Prohibited field collected: {field}")

        # Check for excessive data collection
        if len(collected_data) > 10:
            violations.append("Excessive data collection detected")

        # Check for unnecessary personal information
        unnecessary_fields = ['age', 'gender', 'race', 'religion']
        for field in unnecessary_fields:
            if field in collected_data:
                violations.append(f"Unnecessary personal information collected: {field}")

        return len(violations) == 0, violations

    def minimize_collected_data(self, data, purpose):
        minimized_data = {}

        if purpose == 'authentication':
            minimized_data = {k: v for k, v in data.items()
                            if k in ['email', 'password']}
        elif purpose == 'trading':
            minimized_data = {k: v for k, v in data.items()
                            if k in ['email', 'trading_preferences']}
        elif purpose == 'support':
            minimized_data = {k: v for k, v in data.items()
                            if k in ['email', 'issue_description']}

        return minimized_data

Data Retention Management

class DataRetentionManager:
    def __init__(self):
        self.retention_policies = {
            'user_accounts': 7 * 365,  # 7 years
            'trading_history': 10 * 365,  # 10 years
            'audit_logs': 7 * 365,  # 7 years
            'support_tickets': 3 * 365,  # 3 years
            'marketing_data': 2 * 365,  # 2 years
        }

    def get_retention_period(self, data_type):
        return self.retention_policies.get(data_type, 365)  # Default 1 year

    def should_delete_data(self, data_type, creation_date):
        retention_days = self.get_retention_period(data_type)
        age_days = (datetime.utcnow() - creation_date).days

        return age_days > retention_days

    def schedule_data_deletion(self, data_type, data_id, creation_date):
        retention_days = self.get_retention_period(data_type)
        deletion_date = creation_date + timedelta(days=retention_days)

        # Schedule deletion task
        self._schedule_deletion_task(data_id, deletion_date)

    def delete_expired_data(self):
        expired_data = self._find_expired_data()

        for data_item in expired_data:
            self._delete_data_item(data_item['id'])
            self._log_deletion(data_item)

User Consent Management

Consent Tracking

class ConsentManager:
    def __init__(self):
        self.consent_types = [
            'data_processing',
            'marketing_communications',
            'analytics_tracking',
            'third_party_sharing',
            'cookies'
        ]

    def record_consent(self, user_id, consent_type, granted, timestamp=None):
        if timestamp is None:
            timestamp = datetime.utcnow()

        consent_record = {
            'user_id': user_id,
            'consent_type': consent_type,
            'granted': granted,
            'timestamp': timestamp,
            'ip_address': self._get_user_ip(),
            'user_agent': self._get_user_agent()
        }

        self._store_consent_record(consent_record)
        return consent_record

    def get_user_consent(self, user_id, consent_type):
        consent_records = self._get_consent_records(user_id, consent_type)

        if not consent_records:
            return None

        # Get most recent consent
        latest_consent = max(consent_records, key=lambda x: x['timestamp'])
        return latest_consent['granted']

    def withdraw_consent(self, user_id, consent_type):
        self.record_consent(user_id, consent_type, False)

        # Process consent withdrawal
        if consent_type == 'data_processing':
            self._initiate_data_deletion(user_id)
        elif consent_type == 'marketing_communications':
            self._unsubscribe_from_marketing(user_id)

    def validate_consent(self, user_id, action):
        required_consents = self._get_required_consents(action)

        for consent_type in required_consents:
            if not self.get_user_consent(user_id, consent_type):
                return False, f"Consent required for {consent_type}"

        return True, "All required consents granted"

GDPR Compliance

Data Subject Rights

Right to Access

class DataSubjectAccess:
    def __init__(self, data_manager):
        self.data_manager = data_manager

    def generate_data_export(self, user_id):
        # Collect all user data
        user_data = {
            'personal_information': self._get_personal_info(user_id),
            'trading_history': self._get_trading_history(user_id),
            'account_activity': self._get_account_activity(user_id),
            'communications': self._get_communications(user_id),
            'consent_records': self._get_consent_records(user_id)
        }

        # Generate export file
        export_file = self._create_export_file(user_data)
        return export_file

    def _get_personal_info(self, user_id):
        return self.data_manager.get_user_profile(user_id)

    def _get_trading_history(self, user_id):
        return self.data_manager.get_trading_history(user_id)

    def _get_account_activity(self, user_id):
        return self.data_manager.get_account_activity(user_id)

    def _get_communications(self, user_id):
        return self.data_manager.get_communications(user_id)

    def _get_consent_records(self, user_id):
        return self.data_manager.get_consent_records(user_id)

Right to Rectification

class DataRectification:
    def __init__(self, data_manager):
        self.data_manager = data_manager

    def rectify_user_data(self, user_id, corrections):
        # Validate corrections
        validated_corrections = self._validate_corrections(corrections)

        # Apply corrections
        for field, new_value in validated_corrections.items():
            self.data_manager.update_user_field(user_id, field, new_value)

        # Log rectification
        self._log_rectification(user_id, corrections)

        return True

    def _validate_corrections(self, corrections):
        validated = {}

        for field, value in corrections.items():
            if self._is_valid_field(field) and self._is_valid_value(field, value):
                validated[field] = value

        return validated

Right to Erasure (Right to be Forgotten)

class DataErasure:
    def __init__(self, data_manager):
        self.data_manager = data_manager

    def erase_user_data(self, user_id, reason):
        # Check if erasure is legally required
        if not self._is_erasure_required(user_id, reason):
            return False, "Erasure not legally required"

        # Anonymize or delete data
        self._anonymize_user_data(user_id)

        # Log erasure
        self._log_erasure(user_id, reason)

        return True, "Data erased successfully"

    def _anonymize_user_data(self, user_id):
        # Anonymize personal data
        self.data_manager.anonymize_user_profile(user_id)

        # Delete sensitive data
        self.data_manager.delete_sensitive_data(user_id)

        # Retain data required by law
        self.data_manager.retain_required_data(user_id)

Data Protection Impact Assessment (DPIA)

DPIA Framework

class DPIAFramework:
    def __init__(self):
        self.risk_factors = [
            'data_volume',
            'data_sensitivity',
            'processing_purpose',
            'data_subjects',
            'retention_period',
            'third_party_sharing'
        ]

    def conduct_dpia(self, processing_activity):
        # Assess risk factors
        risk_assessment = self._assess_risk_factors(processing_activity)

        # Calculate overall risk score
        overall_risk = self._calculate_overall_risk(risk_assessment)

        # Determine if DPIA is required
        dpia_required = overall_risk > 0.7

        # Generate recommendations
        recommendations = self._generate_recommendations(risk_assessment)

        return {
            'dpia_required': dpia_required,
            'overall_risk': overall_risk,
            'risk_assessment': risk_assessment,
            'recommendations': recommendations
        }

    def _assess_risk_factors(self, processing_activity):
        risk_scores = {}

        for factor in self.risk_factors:
            risk_scores[factor] = self._assess_factor_risk(factor, processing_activity)

        return risk_scores

    def _calculate_overall_risk(self, risk_assessment):
        # Weighted average of risk factors
        weights = {
            'data_volume': 0.15,
            'data_sensitivity': 0.25,
            'processing_purpose': 0.20,
            'data_subjects': 0.15,
            'retention_period': 0.10,
            'third_party_sharing': 0.15
        }

        overall_risk = sum(risk_assessment[factor] * weights[factor]
                          for factor in self.risk_factors)

        return overall_risk

CCPA Compliance

California Consumer Rights

Right to Know

class CCPACompliance:
    def __init__(self, data_manager):
        self.data_manager = data_manager

    def handle_know_request(self, consumer_id):
        # Collect information about data collection
        data_collection_info = {
            'categories_collected': self._get_categories_collected(consumer_id),
            'sources': self._get_data_sources(consumer_id),
            'business_purposes': self._get_business_purposes(consumer_id),
            'third_parties': self._get_third_parties(consumer_id)
        }

        return data_collection_info

    def _get_categories_collected(self, consumer_id):
        categories = [
            'identifiers',
            'personal_information',
            'commercial_information',
            'internet_activity',
            'geolocation_data',
            'sensory_data',
            'professional_information',
            'education_information',
            'inferences'
        ]

        return categories

    def _get_data_sources(self, consumer_id):
        sources = [
            'directly_from_consumer',
            'third_party_data_providers',
            'public_sources',
            'cookies_and_tracking'
        ]

        return sources

Right to Delete

class CCPADeletion:
    def __init__(self, data_manager):
        self.data_manager = data_manager

    def handle_deletion_request(self, consumer_id):
        # Verify consumer identity
        if not self._verify_consumer_identity(consumer_id):
            return False, "Identity verification failed"

        # Check for exceptions
        exceptions = self._check_deletion_exceptions(consumer_id)
        if exceptions:
            return False, f"Deletion not possible due to: {exceptions}"

        # Delete consumer data
        self._delete_consumer_data(consumer_id)

        return True, "Data deleted successfully"

    def _check_deletion_exceptions(self, consumer_id):
        exceptions = []

        # Check for legal obligations
        if self._has_legal_obligations(consumer_id):
            exceptions.append("legal_obligations")

        # Check for business purposes
        if self._has_business_purposes(consumer_id):
            exceptions.append("business_purposes")

        # Check for security purposes
        if self._has_security_purposes(consumer_id):
            exceptions.append("security_purposes")

        return exceptions

Data Breach Response

Breach Detection

Automated Monitoring

class BreachDetection:
    def __init__(self):
        self.monitoring_rules = [
            'unusual_data_access',
            'unauthorized_login_attempts',
            'data_exfiltration_patterns',
            'system_intrusions'
        ]

    def monitor_for_breaches(self):
        alerts = []

        for rule in self.monitoring_rules:
            if self._check_rule(rule):
                alerts.append(self._create_alert(rule))

        return alerts

    def _check_rule(self, rule):
        if rule == 'unusual_data_access':
            return self._check_unusual_access()
        elif rule == 'unauthorized_login_attempts':
            return self._check_login_attempts()
        elif rule == 'data_exfiltration_patterns':
            return self._check_exfiltration_patterns()
        elif rule == 'system_intrusions':
            return self._check_system_intrusions()

        return False

Breach Response

Incident Response Plan

class BreachResponse:
    def __init__(self):
        self.response_steps = [
            'contain_breach',
            'assess_impact',
            'notify_authorities',
            'notify_affected_users',
            'remediate_vulnerabilities',
            'document_incident'
        ]

    def handle_breach(self, breach_details):
        response_log = []

        for step in self.response_steps:
            result = self._execute_step(step, breach_details)
            response_log.append({
                'step': step,
                'result': result,
                'timestamp': datetime.utcnow().isoformat()
            })

        return response_log

    def _execute_step(self, step, breach_details):
        if step == 'contain_breach':
            return self._contain_breach(breach_details)
        elif step == 'assess_impact':
            return self._assess_impact(breach_details)
        elif step == 'notify_authorities':
            return self._notify_authorities(breach_details)
        elif step == 'notify_affected_users':
            return self._notify_affected_users(breach_details)
        elif step == 'remediate_vulnerabilities':
            return self._remediate_vulnerabilities(breach_details)
        elif step == 'document_incident':
            return self._document_incident(breach_details)

Best Practices

Data Protection Checklist

Implementation Checklist

✅ Data Classification: Classify all data by sensitivity level
✅ Encryption: Encrypt data at rest and in transit
✅ Access Controls: Implement role-based access controls
✅ Audit Logging: Log all data access and modifications
✅ Data Minimization: Collect only necessary data
✅ Retention Policies: Implement data retention policies
✅ Consent Management: Track and manage user consent
✅ Breach Response: Have incident response plan ready
✅ Regular Audits: Conduct regular security audits
✅ Staff Training: Train staff on data protection

Compliance Guidelines

GDPR Compliance: Implement all GDPR requirements
CCPA Compliance: Meet California Consumer Privacy Act requirements
Financial Regulations: Comply with financial data protection laws
Regular Updates: Keep compliance measures updated
Documentation: Maintain comprehensive documentation
Testing: Regularly test compliance measures

Ready to ensure compliance? Check out our Compliance guide.