Data Protection & Privacy

Synthetic Data Generation

  • May 9, 2025
  • 0

Sherlocked Security – Synthetic Data Generation

Create Privacy-Preserving, Realistic Data for Secure Development and Testing

1. Statement of Work (SOW)

Service Name: Synthetic Data Generation
Client Type: Enterprises, Healthcare Providers, Financial Institutions, Software Developers, AI/ML Teams
Service Model: Project-Based Implementation & Retainer Consulting
Compliance Alignment: GDPR, CCPA, HIPAA, NIST 800-53, ISO/IEC 27001, SOC 2

Synthetic Data Generation Covers:

  • Creation of realistic, privacy-preserving synthetic data for testing, training, and development
  • Tailored generation of synthetic datasets based on real data patterns, but without any personally identifiable information (PII)
  • Integration with machine learning (ML) and artificial intelligence (AI) models for enhanced testing and model training
  • Automation of synthetic data pipelines to support continuous integration/continuous deployment (CI/CD) in development environments
  • Customization of synthetic data to match specific business scenarios, regulatory requirements, or system needs
  • Support for compliance with data protection laws while leveraging the utility of realistic data in development and testing

2. Our Approach

[Data Analysis] → [Synthetic Data Design] → [Generation & Integration] → [Compliance Mapping] → [Testing & Validation] → [Ongoing Monitoring & Reporting]

3. Methodology

  • Data Analysis:

    • Understand the client’s data requirements, including the types of data to be generated, their format, and any regulatory constraints.
    • Perform an analysis of the original dataset to capture key statistical properties (e.g., distributions, correlations, and categorical patterns) that need to be preserved in synthetic data.
    • Identify data privacy concerns and ensure that generated synthetic data does not contain any real PII.

  • Synthetic Data Design:

    • Design the synthetic data model based on the real-world data patterns.
    • Ensure that the synthetic data is realistic, preserving the data distributions, relationships, and structures found in the original dataset.
    • Leverage advanced generative models such as GANs (Generative Adversarial Networks) or other AI-based techniques to generate high-quality synthetic datasets.

  • Generation & Integration:

    • Generate synthetic data using the selected methodology (e.g., GANs, rule-based systems, or statistical models).
    • Ensure the synthetic data is integrated into the client’s testing, development, or training workflows, including seamless support for AI/ML model development and validation.
    • Automate synthetic data generation pipelines for continuous testing and model retraining.

  • Compliance Mapping:

    • Ensure that synthetic data generation complies with applicable privacy laws and regulations (GDPR, HIPAA, CCPA, etc.).
    • Verify that synthetic data cannot be reverse-engineered or linked back to any individual or organization in the original data.
    • Provide validation that the synthetic data does not inadvertently expose sensitive or confidential information.

  • Testing & Validation:

    • Validate that the synthetic data preserves the required properties of the original data while ensuring privacy.
    • Perform testing to ensure that synthetic data maintains its utility for model training, algorithm testing, and performance evaluations.
    • Conduct anonymization checks and privacy assessments to ensure that the synthetic data complies with data protection laws.

  • Ongoing Monitoring & Reporting:

    • Provide continuous monitoring to ensure that synthetic data generation pipelines remain effective and compliant.
    • Offer periodic reporting on the effectiveness of the synthetic data models and their usage within the client’s workflows.
    • Make recommendations for improvements or adjustments based on new data patterns or evolving compliance requirements.

4. Deliverables to the Client

  1. Synthetic Data Design Report: A comprehensive report outlining the design process, data patterns preserved, and methodology used for synthetic data generation.
  2. Synthetic Data Generation Framework: A framework for generating synthetic data tailored to the client’s specific needs, with automation capabilities.
  3. Data Privacy & Compliance Validation: A report demonstrating that synthetic data complies with privacy laws and cannot be reverse-engineered to reveal real PII.
  4. Generated Synthetic Data Datasets: A set of realistic synthetic datasets ready for use in testing, training, or development.
  5. Integration & Testing Documentation: A report detailing the integration of synthetic data into testing workflows, including model training, validation, and performance testing.
  6. Ongoing Monitoring Dashboard: A dashboard for monitoring the usage and effectiveness of synthetic data in development or testing processes.

5. What We Need from You (Client Requirements)

  • Data Sample: A representative sample of the original data (without PII) for the creation of synthetic data.
  • Data Processing Requirements: Detailed description of the data fields, types, and structure needed for synthetic data generation.
  • Compliance Guidelines: Information on any regulatory or compliance requirements that must be met (e.g., GDPR, HIPAA).
  • Integration Requirements: Information on how the synthetic data will be used in development, testing, or model training.
  • Stakeholder Interviews: Access to data owners, security teams, and development teams for insights into specific data usage needs.

6. Tools & Technology Stack

  • Data Generation Tools:
    • DataSynthesizer, Syntho, CTGAN, SDV (Synthetic Data Vault)
  • Generative Models:
    • TensorFlow, PyTorch, Keras (for GAN-based generation)
  • Compliance & Privacy Tools:
    • OpenDP, ARX Data Anonymization, Tonic.ai
  • Data Integration:
    • Apache Kafka, Apache Spark, dbt (Data Build Tool)
  • Automation:
    • Airflow, Jenkins, GitLab CI/CD (for automating data pipelines)

7. Engagement Lifecycle

  1. Kickoff & Scoping: Initial meeting to understand client’s data needs, compliance requirements, and synthetic data objectives.
  2. Data Analysis: Review of the original data and its properties to be preserved in synthetic datasets.
  3. Synthetic Data Design: Creation of a tailored design for generating synthetic data.
  4. Generation & Integration: Deploy synthetic data generation models and integrate with testing or training workflows.
  5. Compliance & Privacy Validation: Conduct privacy assessments to ensure compliance with relevant laws and data protection regulations.
  6. Testing & Performance Validation: Test synthetic data for accuracy, privacy, and suitability for use in AI/ML models or development environments.
  7. Ongoing Monitoring & Support: Continuous monitoring of synthetic data usage and performance, with regular reporting.

8. Why Sherlocked Security?

Feature Sherlocked Advantage
Privacy-Preserving Data Ensure that synthetic data cannot be reverse-engineered to reveal real user information
Generative AI Expertise Leverage GANs and other AI-based models for high-quality synthetic data generation
End-to-End Data Integration Seamlessly integrate synthetic data into development, testing, and AI/ML workflows
Regulatory Compliance Ensure synthetic data generation complies with GDPR, HIPAA, and other privacy laws
Custom Synthetic Data Models Tailor synthetic datasets to the specific business needs of your organization

9. Real-World Case Studies

Healthcare – Synthetic Patient Data for Research

Client: A healthcare organization conducting medical research.
Findings: The research required large datasets of patient information, but using real patient data posed privacy risks.
Outcome: Generated synthetic patient data that mirrored real-world health records, enabling the research team to conduct studies without compromising patient privacy.

Financial Institution – Secure Model Training with Synthetic Transaction Data

Client: A global bank developing fraud detection models.
Findings: The bank needed large datasets of transaction information for model training but couldn’t use real transaction data due to privacy concerns.
Outcome: Created synthetic transaction data based on real-world patterns, providing the bank with sufficient data to train and test fraud detection models securely.

10. SOP – Standard Operating Procedure

  1. Initial Discovery: Define the scope, objectives, and compliance requirements for synthetic data generation.
  2. Data Analysis: Analyze the original dataset to identify patterns, distributions, and correlations that must be maintained.
  3. Synthetic Data Design: Design the synthetic data model, selecting the appropriate techniques and tools.
  4. Data Generation: Generate synthetic data using the chosen model, ensuring privacy and compliance.
  5. Integration: Integrate synthetic data into client workflows for testing, training, or validation.
  6. Compliance Validation: Perform checks to ensure synthetic data complies with privacy laws and cannot be traced back to individuals.
  7. Ongoing Monitoring: Set up monitoring tools to ensure synthetic data continues to meet client needs and privacy requirements.

11. Synthetic Data Generation Readiness Checklist

1. Pre-Implementation Preparation

  • [ ] Sample of original data (without PII)
  • [ ] Data usage requirements (e.g., testing, training, validation)
  • [ ] Compliance guidelines and privacy regulations
  • [ ] Integration and automation requirements

2. During Engagement

  • [ ] Design synthetic data model based on original data patterns
  • [ ] Generate and validate synthetic datasets for privacy and utility
  • [ ] Integrate synthetic data into workflows for testing or AI/ML model training

3. Post-Implementation Actions

  • [ ] Monitor synthetic data usage and privacy compliance
  • [ ] Provide periodic reports on synthetic data effectiveness
  • [ ] Adjust synthetic data models as needed based on evolving requirements
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