Ika Network: A new sub-second MPC infrastructure for the Sui ecosystem

The Ika network is an innovative MPC infrastructure project supported by the Sui Foundation, with its most notable feature being sub-second response times. Ika and Sui are highly aligned in their underlying design philosophy, and in the future, it will be directly integrated into the Sui development ecosystem, providing plug-and-play cross-chain security modules for Sui Move smart contracts.

Core Technology Highlights

The core technology of the Ika network includes:

1. 2PC-MPC Signature Protocol: Decomposes the user's private key signing operation into a process jointly participated by the user and the Ika network, employing a broadcast model to enhance efficiency.

2. Parallel Processing: By utilizing Sui's object parallel model, the signing operation is decomposed into multiple concurrent subtasks that are executed simultaneously, significantly increasing speed.

3. Large-scale node network: Supports thousands of nodes participating in signing, with each node holding only a part of the key fragment, enhancing security.

4. Cross-chain Control and Chain Abstraction: Allowing smart contracts on other chains to directly control accounts in the Ika network, simplifying cross-chain interaction processes.

The Impact of Ika on the Sui Ecosystem

1. Bring cross-chain interoperability capabilities to Sui, supporting low-latency access to the Sui network for assets such as Bitcoin and Ethereum.

2. Provide a decentralized asset custody mechanism, which is more flexible and secure than traditional centralized custody.

3. Simplify cross-chain interaction processes, allowing smart contracts on Sui to directly operate accounts and assets on other chains.

4. Provide a multi-party verification mechanism for AI automated applications to enhance transaction security and credibility.

Challenges Faced by Ika

1. Competing with existing cross-chain solutions requires seeking a balance between decentralization and performance.

2. The issue of revoking signature permissions in the MPC scheme still needs to be addressed.

3. Dependence on the stability of the Sui network and the need to adapt to future upgrades of Sui.

4. The complexity of the network and the challenges of transaction ordering brought by the DAG consensus model.

Comparison of Privacy Computing Technologies: FHE, TEE, ZKP, and MPC

Technical Overview

• FHE: Allows arbitrary computations on encrypted data with full confidentiality, but the computational overhead is high.
• TEE: Utilizes trusted hardware modules to isolate execution, performance is close to native, but relies on hardware trust.
• MPC: Multi-Party Computation, no need for a single point of trust, but communication overhead is high.
• ZKP: Prove knowledge of certain information without disclosing details, applicable for verification.

Applicable Scenario Comparison

1. Cross-chain signature:

   • MPC is most suitable, such as the 2PC-MPC parallel signature of the Ika network.
   • TEE is also available, but there are hardware trust risks.
   • FHE theory is feasible but too costly.

2. DeFi Multisignature and Custody:

   • Mainstream MPC, such as Fireblocks' distributed signing.
   • TEE is used for hardware wallets, but there are trust issues.
   • FHE is mainly used for upper-level privacy logic.

3. AI and Data Privacy:

   • FHE has obvious advantages with fully encrypted computation.
   • MPC can be used for federated learning, but the cost of multi-party collaboration is high.
   • TEE has memory limitations and side-channel risks.

Plan Differences

1. Performance and latency: TEE is the fastest, FHE is the slowest, ZKP and MPC are in the middle.
2. Trust assumptions: FHE and ZKP are based on mathematics, TEE relies on hardware, and MPC depends on the behavior of participants.
3. Scalability: ZKP and MPC are easily scalable, while FHE and TEE are resource-constrained.
4. Integration Difficulty: TEE is the easiest, ZKP and FHE require specialized circuits, and MPC requires protocol stack integration.

Market View

The statement that "FHE is superior to other solutions" is not entirely accurate. There are trade-offs among various technologies in terms of performance, cost, and security, and there is no absolute optimal solution. The future trend may be the complementary integration of multiple technologies, such as Nillion combining MPC, FHE, TEE, and ZKP, to balance various demands. The choice of technology should be determined based on specific application needs and performance trade-offs.