Content Hub
Insights, updates, and deep dives from the Partisia Blockchain ecosystem.
Building Digital Trust Through Privacy-First Innovation
Explore ideas, research, and real progress shaping a more private, secure, and decentralised digital future.
Join our growing community and engage with builders.
Latest Articles
& Insights
Browse all published posts covering ecosystem updates, technical breakthroughs, research, and community perspectives driving the continued growth of Partisia Blockchain and privacy-first decentralised technologies.
Partisia delivers MPC as a Service, using blockchain coordination and MPC nodes to run privacy-preserving computations securely on secret inputs across decentralized infrastructure.
The SPDZ protocol in MPC enables secure multi-party computation with malicious security, combining secret sharing and authentication to ensure correctness, integrity, and privacy.
Zero-knowledge proofs in MPC enable verification of claims without revealing data, strengthening privacy, blockchain validation, and protection against malicious behavior in secure computation.
Homomorphic encryption in MPC enables computation on encrypted data, allowing results to be derived without revealing inputs, supporting secure collaboration and privacy-preserving analytics.
Garbled circuits in MPC enable two parties to compute functions securely, revealing only outputs while keeping inputs private through encrypted circuit evaluation.
Oblivious Transfer in MPC enables parties to exchange selected secrets privately, forming a fundamental building block for secure computation without revealing inputs.
Explains Beaver’s Trick, a core MPC technique enabling secure multiplication of secret values efficiently, forming a foundation for scalable privacy-preserving computation.
Shamir secret sharing in MPC distributes secrets as polynomial shares, enabling threshold reconstruction and secure computation on hidden values without exposing underlying data.
Secret sharing in MPC enables data to be split across parties, allowing secure computation on private inputs without revealing underlying information.