Many friends who see the news of Nillion raising $25M may be curious about WTF is "Blind Compute"? With some understanding of obscure concepts like MPC, ZKP, FHE, and TEE, a brand new concept has emerged. So, what is the general workflow of Blind Compute? What exactly is the blind computing solution provided by Nillion? Next, I will share my understanding:

1) What is Blind Compute? Simply put, Blind Compute is a secure computing method that allows the server (node) to perform computational tasks on a segment of encrypted data, ultimately achieving privacy protection.

Like ZKP, TEE, MPC, and FHE, the goals of enhanced encryption algorithms are consistent, but the differences lie in: ZKP zero-knowledge proofs require significant overhead to generate proofs, making them suitable for off-chain storage and computation, with only verification on-chain, such as in Rollup Layer2; TEE (Trusted Execution Environment) is a method that relies on hardware vendors to perform computations in an isolated environment; FHE (Fully Homomorphic Encryption) allows computations to be performed directly on encrypted data, but currently only supports specific operations.

"Blind Compute" is a more general computational framework because encryption technologies like ZKP, TEE, and FHE can all potentially be part of its technical framework.

It is well known that ZKP, TEE, and FHE are currently in the exploration and optimization phase of integrating with crypto technology applications. Blind Compute may potentially aggregate these core encryption technologies to explore an integrated engineering practice solution for privacy protection.

2) The core logic of Blind Compute is to enhance distributed nodes, allowing a single node to simultaneously possess the capabilities of segmented storage and computation, along with a verifiable open governance network, thereby achieving effective results without the nodes knowing the "complete" data. How to understand this?

Typically, protecting data privacy requires storing data on Node A, then encrypting it and handing it over to Node B for computation, followed by decryption and verification by Node C to complete the data storage and computation tasks. This process incurs significant costs in data transmission, and the repeated Encrypt -> Decrypt process poses risks of data exposure, with high mutual trust costs between nodes, making it difficult to ensure privacy is not leaked.

Nillion's business logic precisely addresses this flaw, with a general workflow (for understanding purposes):

Nillion has built a distributed node network, where each node has enhanced storage and computation capabilities. When the Nillion network receives a data transmission processing request, it first executes a compilation preprocessing using a specific language called Nada, which splits the original data into many segments, all in an encrypted state.

Then, the AIVM virtual machine schedules and allocates these tasks, with distributed nodes randomly storing and computing these data segments, ultimately completing aggregation and unified verification. Throughout the entire process, a single node cannot know the complete data content, yet when pieced together, it can achieve the overall encrypted transmission and computation of the data.

Why is it said that Blind Compute can aggregate the applications of technologies like ZKP, TEE, and FHE? The logic is simple: during the data preprocessing, which is the encryption phase, FHE homomorphic encryption technology can be fully applied, while the storage and computation of data by nodes can occur in a TEE trusted execution environment. When aggregating and verifying the results of the nodes' work, ZKP can be used to enhance the efficiency of verification aggregation.

3) In my view, technologies like ZKP, TEE, FHE, and MPC all have some engineering implementation flaws to varying degrees. Currently, almost every track in the crypto field is crowded with projects, but they are mostly focused on cost and efficiency optimization, concentrating on specific crypto application scenarios.

The Blind Compute framework proposed by Nillion, although not yet implemented on a large scale, has an integrated encryption solution that may be widely adopted in broader data protection fields such as AI verifiable computing and machine learning.

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