In traditional social networking and matching platforms, users must share sensitive personal data with central servers to find meaningful connections. This creates significant privacy risks:

Personal preferences and data are exposed to platform operators
Vulnerability to data breaches and misuse
No control over how personal information is used for matching
Risk of data being sold or shared with third parties
Potential for discrimination based on sensitive attributes

SocialMPC is a social platform that uses Multi-Party Computation (MPC) to enable privacy-preserving connections between users. Users can find compatible matches based on skills, interests, and preferences without revealing their actual data to anyone – not even the platform itself. -

Privacy-First Match Making
A matching system that finds ideal connections based on skills, interests, and experience while keeping personal data encrypted. Enables precise matching for both professional needs and personal interests without compromising privacy.
Seamless Third-Party Platform Integration
Securely connects with platforms like Spotify, YouTube, and professional networks to enhance matching profiles. Analyzes compatibility across platforms using MPC, ensuring no platform can see another's data.
Complete User Data Sovereignty
Users maintain full control with granular privacy settings and encrypted data processing. Includes a privacy-preserving chat system where users communicate securely before choosing to reveal information.
Zero-Knowledge Trust System
Innovative verification system that proves credentials without revealing them, using zero-knowledge proofs. Features anonymous feedback and reputation scoring while maintaining complete privacy.

Due to lack of proper development resources, it is quite hard to implement everything i thought i can implement - faced difficulty in implementing custom MPC implementation.