As organizations strive to leverage data for AI, privacy concerns and regulations like GDPR and the EU AI Act pose challenges. Synthetic data emerges as a solution, enabling organizations to train AI models, comply with regulations, and explore new possibilities without compromising individual privacy.
Microsoft’s Phi-3 small language model demonstrates the power of synthetic data in creating language models without compromising privacy. However, synthetic data has limitations. It can be challenging to generate diverse and specific data while maintaining privacy.
Differentially private (DP) synthetic data generation presents a promising solution by ensuring privacy while creating statistically similar data to the original. Recent research papers propose innovative techniques in this area:
1. Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe introduces a method to fine-tune language models on private data with strong privacy guarantees.
2. Differentially Private Synthetic Data via Foundation Model APIs 1: Images and 2: Text present approaches using differential privacy sampling to generate image and text data from foundation models’ inference APIs.
3. Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation explores synthesizing demonstration examples with privacy guarantees for in-context learning.
These advancements show promising results in generating synthetic data with strong privacy guarantees. While these approaches have limitations, their potential to produce realistic data while maintaining privacy is significant.
How can we further advance the generation of synthetic data to balance privacy and innovation in AI development? #SyntheticDataGeneration #DifferentialPrivacy