Multiparty
Computation
Compute on sensitive data without revealing it, enabling verifiable outcomes, shared trust, and real-world collaboration without intermediaries or data exposure.
Confidential computation, built with flexibility.
Multiparty computation enables multiple parties to compute on shared data without revealing their individual inputs. Execution is distributed across independent nodes, enforcing privacy through cryptographic guarantees rather than trusted intermediaries.
Partisia’s implementation makes confidential computation usable across decentralised and enterprise systems, whether deployed on-chain or off-chain, across new or existing technology stacks.
MPC built different
Full MPC Computation
Supports both binary and algebraic circuits, enabling real computation, not just secret sharing or key management.
Blockchain-Level Security.
MPC protects core blockchain security, including consensus, oracle keys, and cross-chain bridge operations.
Programmable MPC Logic
Define what stays private, how computation runs, and who sees results, directly within smart contracts.
Why it matters?
Use sensitive data safely, enabling compliant collaboration, decentralised trust, and real-world applications at scale.
Delivery Models
Deploy MPC on-chain or off-chain, alongside new or existing systems, without changing how your applications operate.
Use Cases
Explore real-world applications enabled by confidential computation across industries and environments.
Why it matters?
Use sensitive data safely, enabling compliant collaboration, decentralised trust, and real-world applications at scale.
Delivery Models
Deploy MPC on-chain or off-chain, alongside new or existing systems, without changing how your applications operate.
Use Cases
Explore real-world applications enabled by confidential computation across industries and environments.
Dimension
Standard Encryption
TEE
ZKP
FHE
MPC (general)
Partisia MPC (Real MPC)
Primary purpose
Protect data at rest/in transit
Protected execution via enclaves
Prove correctness without revealing data
Compute on encrypted data
Compute jointly on private inputs
Programmable private computation as infrastructure
Protects data during computation
No
Yes (inside enclave)
Not computation; proof of computation
Yes (stays encrypted)
Yes (inputs remain private)
Yes, privacy enforced by default
Trust model
Trusted operator
Hardware vendor + attestation
Cryptography (sometimes setup assumptions)
Cryptography + key holder
Threshold of MPC participants
Decentralised MPC nodes + blockchain security
Single point of failure
Yes
Limited (attestation)
Strong (cryptographic proofs)
Weak alone (often paired)
Depends on protocol + logging
Strong: verifiable outcomes + auditable coordination
General-purpose computation
Yes (but not private)
Yes
No (proof system)
Yes (but heavy)
Yes (protocol dependent)
Yes: binary + algebraic circuits supported
Multi-party collaboration
No
Limited
Limited (verification-centric)
Limited (workflow-heavy)
Yes
Yes, core design goal
Performance profile
High
High
Proof generation costly
Very costly
Varies widely
Optimised: fastest MPC class with balanced security
Engineering complexity
Low
Medium
High (circuits/provers)
High (tooling/perf)
Medium–High
Lowered via programmable MPC in smart contracts
Typical “MPC” confusion
N/A
N/A
N/A
N/A
Often used to mean key management only
Not key management-only: full private compute
Where it fits best
Storage, transport security
Hybrid/off-chain secure compute
Compliance proofs, identity claims
Niche encrypted ML
Private collaboration + compute
Production systems: DeFi, identity, fraud, voting, data markets
How it composes with others
Baseline layer
Can wrap MPC/ZK
Complements MPC (proofs)
May pair with MPC/ZK
Often paired with ZK/TEE
Can produce ZK-like verifiability without using ZKPs directly
Talk to The Team
Whether you’re building, exploring, or curious, our team is here to talk through what’s possible.