3.1 ZK Data Rooms to train AI models

Secure hyper-scalable cloud environments in which encrypted datasets are processed to train models as per the smart contract. AI or ML Models can be trained without the enterprise or company or organisation or research lab getting access to the actual data. Zero Knowledge Data rooms enable training of AI models such that data source and data consumer can sign a smart contract in which terms of engagement are explicitly written such as datasets being shared, differential privacy parameters, and terms of sharing including frequency, privacy, payments. Differential Privacy is a mathematical guarantee for concealment of solitary sensitive data point within a particular dataset [25-26]. The approach ensures that AI models can be trained with zero privacy loss. The smart contract makes the transactions and the overall ecosystem tamper-proof and verifiable by regulators.

How it works:

1. Data Preparation: Data owners contribute their data to the ZKDR. The data is pre-processed and converted into a format suitable for ZK proofs.

2. Proof Generation: The data owner generates cryptographic proofs demonstrating specific properties of the data without revealing the data itself. For instance, a proof might show the data includes a certain number of entries or belongs to a specific category.

3. Model Training: The AI model is trained using the proofs instead of the raw data. The model can access the information needed for learning while the underlying data remains hidden.

4. Verification: After training, the model's performance can be verified using additional ZK proofs. This ensures the model is trained on data with the claimed properties.

Value propositions:

1. Enhanced Data Security: ZKDRs protect sensitive data during AI model training. The model can learn from the data's patterns without ever directly accessing it. This is crucial for data containing sensitive information like health records or financial data.

2. Facilitates Collaboration: ZKDRs enable collaboration on AI projects between organizations. Each party can contribute their data without revealing it to others. This allows for building better models by leveraging a wider range of data sources.

3. Improved Regulatory Compliance: ZKDRs can help meet regulations requiring data privacy and model fairness. By not revealing the underlying data, ZKDRs make it harder to identify biases or privacy violations within the training data.