Cifer develops a decentralized federated learning platform that utilizes blockchain technology and fully homomorphic encryption to enable secure, collaborative AI model training without exposing raw data. This approach allows AI developers to access diverse datasets while maintaining data ownership and privacy, facilitating innovation in artificial intelligence.
Funding
$650K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
AI model training requires access to diverse datasets, but sharing raw data exposes sensitive information and raises privacy concerns. Traditional centralized approaches create data silos, limiting collaboration and hindering the development of robust, generalizable AI models.
Solution
Cifer provides a decentralized federated learning platform that leverages blockchain technology and fully homomorphic encryption (FHE) to enable secure, collaborative AI model training without exposing the underlying raw data. The platform allows AI developers to access and aggregate insights from diverse datasets while ensuring data ownership and privacy for contributors. By utilizing FHE, Cifer enables computation on encrypted data, maximizing privacy and security. The Cifer blockchain ensures data integrity and allows data contributors to monetize their data through smart contracts.
Target Audience
Cifer targets AI developers seeking access to diverse datasets for training robust models, as well as data contributors who want to maintain control and monetize their data while preserving privacy.
Features
- Decentralized federated learning for collaborative AI model training
- Fully homomorphic encryption for computing on encrypted data
- Blockchain-based data provenance and tamper-proofing
- Secure aggregation of insights without exposing raw data
- Smart contracts for data monetization and access control
- Python package (`pip install cifer`) for easy integration