SecretService

Inspiration

Financial institutions are fighting a losing battle against AI-powered fraud, which now costs the global economy over $500 billion annually. The most frustrating part is that the data to stop these criminal networks exists, but it is trapped. A regional bank only sees its own fraudulent transactions, creating a massive blind spot. The obvious solution is to pool data across multiple banks to train a unified AI model. However, doing so triggers catastrophic regulatory penalties under frameworks like GDPR, with fines reaching hundreds of millions of dollars. We realized that to solve this paradox, we had to stop trying to move the data securely. Instead, we needed to move the intelligence.

What it does

SecretService is a cloud-native B2B data infrastructure platform that enables highly regulated organizations to collaboratively train enterprise-grade AI models without ever exposing, sharing, or moving their raw customer data.

Instead of centralized data pooling, SecretService brings the AI to the data. We push an untrained fraud-detection model directly to each bank's local, secure server. The model learns locally from their proprietary transaction data, and our platform extracts only the updated mathematical weights. These anonymous insights are then aggregated centrally and distributed back to the network. The result is a synchronized, world-class AI model trained on the combined transaction volume of dozens of competitors, achieving maximum predictive accuracy with zero regulatory risk exposure.

How we Plan to built it

We have designed the architecture of SecretService around two core cryptographic and machine learning pillars: Federated Learning (FL) and Zero-Knowledge Proofs (ZKPs).

The Local Nodes: We will build secure local execution environments deployed via Docker containers directly into a client's existing AWS or Azure infrastructure, ensuring raw data never leaves their perimeter.

The ML Engine: The machine learning backend will be built using Python and PyTorch, utilizing existing federated averaging libraries.

The Aggregator: We will develop a central aggregation server that utilizes ZKPs to verify and combine the encrypted mathematical weights uploaded by the local nodes, ensuring that the model updates cannot be reverse-engineered to expose the original transaction data.

Target Impact & Business Value

By mathematically decoupling data privacy from AI performance, SecretService creates a secure ecosystem of collective intelligence. A mid-sized regional bank will be able to deploy a fraud-detection model trained on the combined transaction volume of fifty different banks. This dramatically increases True Positive fraud detection rates while completely eliminating the risk of multi-million dollar GDPR/CCPA compliance fines.