Ika Network: Sub-second MPC Infrastructure for the Sui Ecosystem

1. Overview and Positioning of the Ika Network

Ika Network is an innovative MPC infrastructure that has received strategic support from the Sui Foundation. Its most prominent feature is sub-second response speed, which is a first in MPC solutions. Ika is highly compatible with Sui in underlying design concepts such as parallel processing and decentralized architecture, 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.

From a functional perspective, Ika is building a new type of security verification layer: serving both as a dedicated signing protocol for the Sui ecosystem and providing standardized cross-chain solutions for the entire industry. Its layered design balances protocol flexibility and development convenience, and 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 revolves around high-performance distributed signatures, with key innovations including:

• 2PC-MPC Signing Protocol: Adopts an improved two-party MPC scheme, breaking down the signing process into a collaborative effort between the "user" and the "Ika network".
• Parallel Processing: Utilize parallel computing to decompose a single signature operation into multiple concurrent subtasks, greatly improving speed.
• Large-scale node network: supports thousands of nodes participating in signing, with each node only holding a part of the key shard.
• Cross-chain control and chain abstraction: allows smart contracts on other chains to directly control accounts in the Ika network (dWallet).

1.2 The potential impact of Ika on the Sui ecosystem

The launch of Ika may bring the following impacts to Sui:

1. Enhance cross-chain interoperability capabilities, supporting low latency and high security 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 on assets from other chains.
4. Provide a multi-party verification mechanism for AI automation applications to enhance transaction security.

1.3 Challenges faced by Ika

Although Ika is closely tied to Sui, it still faces challenges in becoming a "universal standard" for cross-chain interoperability.

1. A better balance between decentralization and performance needs to be sought to attract more developers and asset integration.
2. The issue of revoking signature permissions in the MPC scheme still needs to be resolved.
3. There is a reliance on the stability of the Sui network, and future significant upgrades to Sui may require adaptation by Ika.

2. Comparison of Projects Based on FHE, TEE, ZKP, or MPC

2.1 FHE

Zama & Concrete:

• General-purpose compiler based on MLIR
• The "Layered Bootstrapping" strategy reduces latency
• Support "hybrid encoding" to balance performance and parallelism
• The "key packaging" mechanism reduces communication overhead.

Fhenix:

• Optimization for Ethereum EVM instruction set
• Use "Cipher Virtual Register"
• Design off-chain oracle bridging module to reduce on-chain validation costs

2.2 TEE

Oasis Network:

• Introduce the concept of "layered trusted root"
• The ParaTime interface uses Cap'n Proto binary serialization.
• Develop "Durable Log" module to prevent rollback attacks

2.3 ZKP

Aztec:

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

2.4 MPC

Partisia Blockchain:

• Based on the SPDZ protocol extension, adding a "preprocessing module"
• Use gRPC communication, TLS 1.3 encrypted channel
• Support for a dynamic load balancing parallel sharding mechanism

3. Privacy Computing FHE, TEE, ZKP and MPC

3.1 Overview of Different Privacy Computing Solutions

• Fully Homomorphic Encryption ( FHE ): Allows for arbitrary computation in an encrypted state, theoretically complete but with high computational overhead.
• Trusted Execution Environment ( TEE ): An isolated secure area provided by the processor, with performance close to native but relying on hardware trust.
• Multi-Party Secure Computation ( MPC ): Multi-party collaborative computation does not leak private inputs, has no single point of trust but has significant communication overhead.
• Zero-Knowledge Proof ( ZKP ): Verifying a statement as true without disclosing additional information.

Adaptation scenarios for 3.2 FHE, TEE, ZKP, and MPC

Cross-chain signature:

• MPC is suitable for multi-party collaboration, avoiding single point private key exposure.
• TEE can quickly sign through SGX chips, but there are hardware trust issues.
• FHE theory is feasible but has excessive costs.

DeFi scenarios ( multi-signature wallets, vault insurance, institutional custody ):

• MPC is the mainstream method, distributing trust risks.
• TEE is used to ensure signature isolation, but still relies on hardware trust.
• FHE is mainly used for upper-level privacy logic protection.

AI and Data Privacy:

• The advantages of FHE are obvious, enabling fully encrypted computation.
• MPC is suitable for collaborative learning, but the communication cost is high when multiple parties participate.
• TEE can run models directly in a protected environment, but there are issues such as memory limitations.

3.3 Differentiation of Different Plans

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

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

Scalability: ZKP/MPC > FHE/TEE

Integration difficulty: TEE > MPC > ZKP/FHE

4. Discussion on the viewpoint that "FHE is superior to TEE, ZKP, or MPC"

FHE is not superior to other solutions in all aspects. There are trade-offs among technologies in terms of performance, cost, security, and other factors.

• FHE theory provides strong privacy protection, but its low performance limits applications.
• TEE and MPC provide different trust models and deployment convenience.
• ZKP focuses on verifying correctness

The future privacy computing ecosystem may lean towards the complementary and integrated use of various technologies, such as Nillion combining MPC, FHE, TEE, and ZKP. The choice of technology should be determined based on specific application requirements and performance trade-offs, constructing modular solutions.