New Star of Sui Ecosystem: Exploration of Sub-second MPC Network Ika

1. Overview and Positioning of the Ika Network

Ika Network is an innovative infrastructure strategically supported by the Sui Foundation, built on multi-party secure computing (MPC) technology. Its most notable feature is sub-second response speed, which is a first in MPC solutions. Ika is highly aligned with Sui in terms of underlying design philosophy and will be directly integrated into the Sui development ecosystem in the future, providing plug-and-play cross-chain security modules for Sui Move smart contracts.

From a functional perspective, Ika is building a new type of security verification layer: serving as a dedicated signature protocol for the Sui ecosystem while also providing standardized cross-chain solutions for the entire industry. Its layered design takes into account both the flexibility of the protocol and the convenience of development, and it is expected to become an important practical case for the large-scale application of MPC technology in multi-chain scenarios.

1.1 Core Technology Analysis

The technical implementation of the Ika network primarily revolves around high-performance distributed signatures, with its innovation lying in the use of the 2PC-MPC threshold signature protocol combined with Sui's parallel execution and DAG consensus, achieving true sub-second signing capabilities and large-scale decentralized node participation. Ika aims to create a multi-party signature network that meets both ultra-high performance and strict security requirements through the 2PC-MPC protocol, parallel distributed signatures, and close integration with the Sui consensus structure. Its core innovations include:

• 2PC-MPC Signature Protocol: It uses an improved two-party MPC scheme, breaking down the user's private key signing operation into a process involving both "User" and "Ika Network".

• Parallel Processing: By utilizing parallel computing, a single signing operation is decomposed into multiple concurrent subtasks executed simultaneously across nodes, significantly increasing speed.

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

• Cross-chain control and chain abstraction: allowing smart contracts on other chains to directly control accounts in the Ika network (dWallet), achieving cross-chain interoperability.

Can Ika reverse empower the Sui ecosystem?

After Ika goes live, it is expected to expand the capability boundaries of the Sui blockchain and provide support for Sui's ecological infrastructure:

• Bring cross-chain interoperability to Sui, supporting on-chain assets like Bitcoin and Ethereum to access the Sui network with low latency and high security.

• Provides a decentralized asset custody mechanism, which is more flexible and secure compared to traditional centralized custody solutions.

• Design a blockchain abstraction layer to simplify the process of Sui smart contracts operating on other chain accounts and assets.

• Provide a multi-party verification mechanism for AI automated applications to enhance the security and credibility of AI executing transactions.

1.3 Challenges faced by Ika

Although Ika is closely tied to Sui, there are still some challenges to becoming a "universal standard" for cross-chain interoperability:

• A better balance needs to be found between "decentralization" and "performance" to attract more developers and asset integrations.

• The MPC signature permission revocation mechanism needs improvement, and there may be potential security risks.

• Dependence on the stability of the Sui network and its own network status requires adaptation with Sui upgrades.

• The Mysticeti consensus supports high concurrency and low fees, but the lack of a main chain structure may introduce new ordering and security issues.

2. Comparison of projects based on FHE, TEE, ZKP, or MPC

2.1 FHE

Zama & Concrete:

• General-purpose compiler based on MLIR
• Adopting a "Layered Bootstrapping" strategy
• Supports "mixed encoding"
• Provide "key packaging" mechanism

Fhenix:

• Customized optimization for the Ethereum EVM instruction set
• Use "Ciphered Virtual Register"
• Design off-chain oracle bridging module

2.2 TEE

Oasis Network:

• Introduce the concept of "layered trusted root"
• Use the ParaTime interface to ensure efficient cross-ParaTime communication.
• Develop the "Durability Log" module to prevent rollback attacks

2.3 ZKP

Aztec:

• Integrate "incremental recursion" technology
• Implement a parallel depth-first search algorithm using Rust
• Provide "Light Node Mode" to optimize bandwidth

2.4 MPC

Partisia Blockchain:

• Extension based on the SPDZ protocol
• Add "Preprocessing Module" to accelerate online phase computations
• Support dynamic load balancing

3. Privacy Computing: FHE, TEE, ZKP, and MPC

3.1 Overview of Different Privacy Computing Solutions

• Fully Homomorphic Encryption ( FHE ): Allows arbitrary computations on encrypted data without decryption, but the computational overhead is extremely high.

• Trusted Execution Environment ( TEE ): A trusted hardware module provided by the processor, offering performance close to native computing but relying on hardware trust.

• Multi-Party Computation (MPC): Allows multiple parties to jointly compute without revealing private inputs, but communication overhead is significant.

• Zero-Knowledge Proof ( ZKP ): The verifier confirms the truth of a statement without disclosing any additional information.

Adaptation scenarios of 3.2 FHE, TEE, ZKP and MPC

Cross-chain signature:

• MPC is suitable for multi-party collaboration and avoids scenarios where a single point of private key exposure occurs.
• TEE can run signature logic via SGX chips, which is fast but relies on hardware trust.
• FHE does not have an advantage in signature computation.

DeFi Scenarios:

• MPC is suitable for scenarios such as multi-signature wallets, vault insurance, and institutional custody that require risk sharing.
• TEE can be used for hardware wallets or cloud wallet services, but there are hardware trust issues.
• FHE is mainly used to protect transaction details and contract logic.

AI and Data Privacy:

• FHE is suitable for sensitive data processing and can achieve "computation on encrypted data."
• MPC can be used for collaborative learning, but it faces communication costs and synchronization issues.
• TEE can run models directly in a protected environment, but there are issues such as memory limitations.

3.3 Differentiation of Different Solutions

Performance and Latency:

• FHE has higher latency but provides the strongest data protection.
• TEE delay is minimal, close to ordinary execution
• ZKP has controllable delay in batch proofing.
• MPC latency is low to medium, greatly affected by network communication.

Trust Assumption:

• FHE and ZKP are based on mathematical problems and do not require trust in third parties.
• TEE relies on hardware and vendors
• MPC relies on a semi-honest or at most t faulty model

Scalability:

• ZKP Rollup and MPC sharding support horizontal scaling
• The expansion of FHE and TEE needs to consider computational resources and hardware node supply.

Integration Difficulty:

• The minimum access threshold for TEE
• ZKP and FHE require specialized circuits and compilation processes
• MPC requires protocol stack integration and inter-node communication

4. Market Perspectives and Development Trends

Privacy computing technology faces the "impossible triangle" problem of "performance, cost, and security". FHE theoretically provides strong privacy protection, but its low performance limits its promotion. TEE, MPC, or ZKP are more feasible in real-time and cost-sensitive applications.

Different technologies are suitable for different scenarios:

• ZKP is suitable for off-chain complex computation verification.
• MPC is suitable for multi-party shared private state computation.
• TEE is mature in mobile and cloud environments.
• FHE is applicable to extremely sensitive data processing

The future trend may be the complementarity and integration of multiple technologies, rather than a single solution prevailing. For example, Nillion integrates MPC, FHE, TEE, and ZKP to balance security, cost, and performance. The privacy computing ecosystem will lean towards building modular solutions with appropriate technological components.

! Looking at the technical game between FHE, TEE, ZKP and MPC from the sub-second MPC network lka launched by Sui