New Star of the Sui Ecosystem: Ika Network Leads MPC Technology Innovation

The Ika network, strategically supported by the Sui Foundation, has officially unveiled its technical positioning and development direction. As an innovative infrastructure based on multi-party secure computation (MPC), Ika's most prominent feature is its sub-second response speed, which is unprecedented among similar MPC solutions. Ika is highly compatible with Sui in terms of underlying design such as parallel processing and decentralized architecture, and will be directly integrated into the Sui development ecosystem in the future, providing plug-and-play cross-chain security modules for Move smart contracts.

Ika is building a new type of security verification layer, serving both as a dedicated signing protocol for Sui and providing standardized cross-chain solutions for the entire industry. Its layered design balances protocol flexibility with development convenience, and it is expected to become an important practice for the large-scale application of MPC technology in multi-chain scenarios.

Core Technology Innovation

The Ika network revolves around high-performance distributed signatures, with key technological innovations including:

1. 2PC-MPC Signature Protocol: An improved two-party MPC scheme that decomposes the signing operation into a process involving both the user and the network, using broadcast mode to reduce communication overhead.

2. Parallel Processing: Utilize parallel computing to decompose the signing operation into multiple concurrent subtasks, significantly improving speed in conjunction with Sui's object parallel model.

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: Allows smart contracts on other chains to directly control accounts in the Ika network, enabling cross-chain interoperability through the deployment of light clients.

The Impact of Ika on the Sui Ecosystem

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

2. Provide a decentralized custody mechanism to enhance asset security.

3. Simplify cross-chain interaction processes to enable smart contracts to directly operate on assets from other chains.

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

Challenges Faced

1. Need to compete with other mainstream cross-chain solutions, seeking a balance between decentralization and performance.

2. The MPC signature permission revocation mechanism is not yet完善, posing potential security risks.

3. Depend on the stability of the Sui network, and adapt according to Sui upgrades.

4. The DAG consensus model may bring new ordering and security issues, requiring high network activity.

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

Technical Overview

• Fully Homomorphic Encryption ( FHE ): allows arbitrary computations on encrypted data, fully protecting privacy, but with a significant computational overhead.
• Trusted Execution Environment ( TEE ): Runs code in a hardware-isolated area, with performance close to native, but relies on hardware trust.
• Multi-Party Secure Computation ( MPC ): Multi-party collaborative computing does not disclose individual inputs, has no single point of trust, but incurs high communication overhead.
• Zero-Knowledge Proof ( ZKP ): Verifying that a statement is true without revealing additional information, applicable for proving possession of a secret.

Applicable Scenarios

1. Cross-chain signature:
   • MPC is suitable for scenarios involving multi-party collaboration and avoiding single point private key exposure.
   • TEE can be deployed quickly, but trust relies on hardware.

• FHE theory is feasible but has excessive costs.

2. DeFi Multisignature and Custody:
   • MPC is mainly used to diversify risks.

• TEE is used for isolated environments such as hardware wallets.
  • FHE is mainly used to protect transaction details, not for direct custody.

3. AI and Data Privacy:
   • FHE has obvious advantages, encrypting sensitive data throughout the process.
   • MPC is used for federated learning, but there are challenges in multi-party collaboration.
   • TEE can run models directly in a protected environment, but there are memory limitations.

Plan Comparison

1. Performance and Latency: FHE > ZKP > MPC > TEE ( from high to low )

2. Trust Assumption: FHE/ZKP ( mathematical problem ) > MPC ( semi-honest model ) > TEE ( hardware trust )

3. Scalability: ZKP/MPC > FHE/TEE

4. Integration Difficulty: TEE > MPC > ZKP/FHE

Conclusion

Various privacy computing technologies have their advantages and disadvantages, and there is no absolute optimal solution. In the future, there may be a trend towards a complementary integration of multiple technologies, such as the combination of Ika(MPC) and ZKP, or Nillion integrating various privacy technologies. The choice of suitable technology should be based on specific application requirements and performance trade-offs, constructing a modular solution.