Data Privacy 101

Overview of concepts and tips on optimizing Privacy and Accuracy with Synthetic Data

Basic Concepts & Approaches

Q: What levels of privacy protection does Gretel offer and when should each be used?

A: Gretel offers three levels of privacy protection:

1. Level 1 (Basic) - Data masking using Transform API

• Best for: Initial PII removal, development environments

• Use case: Internal analytics with low-sensitivity data

• Limitations: Vulnerable to data linkage, membership inference, and re-identification attacks

• Practical example, consider a healthcare record after using Transform to remove PII attributes:

  Original: {name: "Jane Smith", age: 34, condition: "diabetes", zip: 90210, height: 5'4"} Masked: {age: 34, condition: "diabetes", zip: 90210, height: 5'4"}

  While direct PII is removed, an attacker with access to a voter database could still identify Jane by matching the combination of age, zip code, and height, demonstrating why Transform alone isn't sufficient for sensitive data

• See: Gretel Transform

1. Level 2 - Synthetic data generation

• Best for: Development environments, internal testing

• Use case: Training non-production models, data exploration

• Features: Maintains statistical properties while generating new data, including altering distributions

• See: Gretel Navigator Fine-Tuning

1. Level 3 - Differential privacy-enabled synthetic data

• Best for: Production data, external sharing, regulated industries

• Use case: Training production models, sharing data with partners

• Features: Mathematical privacy guarantees, protection against inference attacks

• See: Gretel Navigator Fine-Tuning or Gretel GPT with differential privacy

Q: What privacy parameters does Gretel use and how do they compare to other organizations?

A: Gretel uses:

• Epsilon (ε) = Configurable based on privacy use cases and utility requirements, we recommend 1.0 (for formal guarantees), 8.0 (balanced), even up to 20 for practical protections with reduced formal guarantees
• Delta (δ) = With Gretel, this is automatically set to 1/n^1.2, where n is the number of examples in the dataset

For comparison:

• US Census Bureau uses ε = 17.14 for US person data

• Google uses ε = 6.92 and δ = 10^-5 for next-word prediction on Android

• Apple's Safari browser uses ε values between 8 and 16

Technical Implementation

Q: What is the technical implementation behind Gretel's differential privacy?

A: Gretel uses:

• Differential Privacy Stochastic Gradient Descent (DP-SGD) algorithm

• Noise addition during optimization

• Gradient clipping to prevent memorization

• Fine-tuning of only ~1% of total model weights

Q: What models does Gretel use for synthetic data generation?

A: For differentially private synthetic data, Gretel uses:

• Open small language models such as Phi or Llama model as the base

• LoRA for efficient fine-tuning

• Training happens on Gretel community cloud or customer infrastructure for hybrid deployments

• Different model architectures supported for different needs (e.g., Navigator Fine-tuning across all text modalities, Gretel GPT for purely text, or Gretel Tabular DP for categorical data)

Q: What are the minimum data requirements for using differential privacy?

A: Gretel recommends:

• At least a few thousand examples for effective DP implementation

• 5,000-8,000 records can provide reasonable performance

• Smaller datasets may require larger epsilon values (8-10) to balance privacy and utility

Regulatory Compliance & Industry Applications

Q: What are the regulatory considerations for using differential privacy?

A: Differential privacy is strongly recommended for:

• GDPR compliance when processing EU citizen data

• HIPAA compliance for healthcare data

• CCPA compliance for California consumer data

• Any regulated industry where data privacy is paramount

Q: What industries benefit most from differential privacy?

A: Key industry applications include:

Healthcare & Life Sciences:

• Sharing electronic health records (EHR)

• Patient diagnosis and symptom analysis

• Treatment research without compromising patient privacy

Financial Services:

• Fraud detection system development

• Customer service chatbot training

• Analysis of customer interactions

Customer Support:

• Training data for support systems

• Analysis of customer feedback

• Call center log processing

Q: What types of text data can benefit from differential privacy?

A: Common text data types include:

• Customer feedback transcripts

• Call center logs

• Internal reports and documents

• Customer reviews

• Chat logs

• Product feedback

• Medical records and patient descriptions

Privacy Measurements & Protection

Q: How does Gretel measure and verify privacy protection?

A: Gretel provides comprehensive privacy measurements:

PII Replay Detection:

• Identifies sensitive information from training data in synthetic output

• Measures unique value overlap between original and synthetic data

• Provides column-level analysis of PII exposure

Privacy Attack Protection:

• Membership inference attack simulation (360 scenarios)

• Attribute inference attack simulation

• Direct data leakage detection

• Privacy scores (optimal range: 60-90)

Q: How should organizations interpret PII replay metrics?

A: Context is crucial when interpreting PII replay:

Expected Replay Rates:

• Common fields (first names, states): Some replay is normal and expected

• Unique identifiers (email, SSNs): Should see zero replay

• Sensitive combinations (full names, age + zip code): Should show significantly reduced replay rates

Example Interpretation:

• First names: 30-40% replay may be acceptable (limited name pool)

• Full names: <1% replay indicates good privacy protection

• Location data: High replay for common fields (states) is expected

Best Practices & Implementation

Q: What are the best practices for minimizing privacy risks while maintaining data utility?

A: Key recommendations include:

1. Pre-processing:

• Run Transform before synthetic generation for privacy-centric use cases

• Remove unnecessary sensitive columns

1. Model Selection:

• Use Navigator Fine Tuning for better privacy

• Enable differential privacy for sensitive data

1. Validation:

• Monitor PII replay metrics

• Test downstream task performance using Gretel Evaluate API

Q: What performance can be expected from differentially private synthetic data?

A: Based on Gretel's testing on the Yelp Restaurant Reviews dataset:

• Achieves downstream accuracy within 1% of non-private models on scaled datasets (100k+ examples), and within 10% of the accuracy for smaller datasets (10k+ examples)

• Synthetic Quality Score (SQS) of 86 out of 100

• Text semantics similarity score of 94/100 compared to real-world data

• Processing 1M reviews (632 MB) takes approximately 40 hrs fine-tuning time on a single A10G in Gretel Cloud, or 12 hours on a single A100.

Q: How should organizations balance privacy and utility?

A: Organizations should:

• Target privacy and utility scores between 60-95+

• Adjust epsilon values based on sensitivity requirements

• Consider downstream use cases when setting privacy parameters

• Use privacy reports to verify protection levels

• Test synthetic data in actual applications to ensure utility

• Collaborate on privacy and evaluation settings with your compliance and InfoSec teams