About us





About us



Self Framework

SelfFramework is a comprehensive Human Data Platform designed to securely handle and share sensitive personal data, with a particular emphasis on medical information. The framework uses blockchain technology, smart contracts and advanced cryptographic methods to ensure the secure collection, storage and sharing of sensitive data, as well as privacy and compliance.




Self Framework SDK

The Self Framework SDK is designed to facilitate seamless integration of third-party applications and services with the Self Network. It provides developers with a set of tools and APIs to interact with the Self Network for secure and privacy-preserving authentication and data sharing.

Self Network

The Self Network is built on a proprietary blockchain (SelfChain) designed specifically for high transaction volumes and robust data security. This state-of-the-art blockchain infrastructure eliminates the need for third-party solutions, providing strong security guarantees and data integrity from the ground up.

The platform integrates advanced encryption techniques, such as AES-256 for data at rest and SSL/TLS for data in transit, to ensure that users’ personal information remains private and secure.

This approach aligns with the SelfFramework’s objectives of providing a privacy-preserving and secure solution for managing digital identity and personal data, leveraging cutting-edge blockchain and cryptographic technologies to ensure data security and compliance with global regulations.

Self Profile

User Interface layer for user interaction and data management. Each Self Profile instance is represented through custom smart contracts on the SelfChain blockchain. These smart contracts provide the necessary functionalities to manage user profiles securely and efficiently.

New Self Profile accounts are created using a factory pattern, which clones a singleton reference implementation. This method ensures the uniqueness of Self DIDs (Decentralized Identifiers) and maintains the integrity of the user profile creation process.

Self Framework Core concepts

Data Security and Encryption

Homomorphic Encryption: Enables secure data analysis without decryption, maintaining data privacy and compliance with data protection regulations.

Zero Knowledge Proofs (ZKP): Ensures data integrity and confidentiality by allowing verifiable computations and proofs without revealing sensitive information.

OSINT (OpenSource INTelligence): Monitors and mitigates threats related to sensitive data leakage, such as ransomware and identity theft. Includes custom OSINT tools and integration with third-party monitoring services. 

Compliance with Regulations

Data Protection: Adheres to global standards such as HIPAA, GDPR, and other relevant regulations to ensure the secure and legal handling of sensitive data.

Data Destruction: Implements controlled data destruction mechanisms to comply with GDPR requirements while maintaining blockchain integrity.

Smart Contracts and NFT Tokens

Dynamic Access Control: NFTs serve as access keys to encrypted personal data, with embedded permissions and rules governing access and usage.

Royalty Enforcement: Smart contracts enforce royalty payments for data access and sharing, ensuring data owners are compensated whenever their data is used.

Revocation Capabilities: The ability to revoke access to data for specific NFTs introduces a dynamic control layer over the data, ensuring compliance with evolving privacy laws and regulations.

Self-Sovereign Identity (SSI)

User Control: SSI gives individuals full control over their personal data and identity credentials, stored in a decentralized manner.

Decentralized Authentication: Enhances security by eliminating the need for central identity providers, reducing the risk of identity theft.

Interoperable Health Records: Facilitates the seamless sharing of health records across different healthcare systems, ensuring continuity of care.

Self AI

Uses Large Language Models (LLMs) and fuzzy ontology for medical data analysis.

Incorporates libraries like TensorFlow and Hugging Face Transformers.

Methods include model training, fine-tuning, Retrieval-Augmented Generation (RAG), and prompting.

Quantum Computing

Quantum computing represents a revolutionary leap in computational power, particularly well-suited for complex data analysis tasks that are infeasible for classical computers. In the context of SelfFramework, integrating quantum computing can significantly enhance our capabilities in analyzing large and complex datasets, particularly medical data, with increased speed and accuracy.