I will secure sensitive financial data preserving ml for predictive analytics

The Problem

Our client, a leading financial institution, faced the challenge of leveraging machine learning for predictive analytics while ensuring the security and privacy of sensitive data. Traditional machine learning models posed limitations in preserving data privacy, especially when dealing with sensitive financial information.

Our Solution

We proposed integrating Fully Homomorphic Encryption (FHE) with the powerful XGBoost model to provide secure and privacy-preserving predictive analytics capabilities. By implementing FHE-XGBoost, we aimed to enable the client to leverage machine learning for decision-making without compromising data privacy.

Tech Stack

Tools used

• SEAL library, XGBoost, PDTE (Predictive Decision Tree Engine), FHE libraries

Language/techniques used

• Python, homomorphic encryption, machine learning model integration

Models used

• FHE-XGBoost

Skills used

• Machine learning, cryptography, software development
• Web Cloud Servers used
• Virtual Machine (Linux)

Business Impact

Enhanced Data Security: By implementing FHE-XGBoost, the client achieved enhanced data security by performing predictive analytics on encrypted data, mitigating the risk of data breaches.