Many frens see the news of Nillion's $25M financing and wonder WTF is 'blind computing'? Just as you start to understand concepts like MPC, ZKP, FHE, and TEE, a brand-new concept emerges. So, what is the workflow of blind computing? What does Nillion's blind computing solution offer? Let's discuss my understanding next:

What is Blind Compute? Simply put, Blind Compute is a secure computing method that allows the server (Node) to perform a computation task on a data fragment in an encryption state, ultimately achieving privacy protection.

The goals of ZKP, TEE, MPC, FHE, and other enhanced Encryption Algorithms are the same, but the differences are as follows: ZKP zero-knowledge proof generation requires huge overheads, suitable for off-chain storage + computation, and on-chain only verification scenarios, such as Rollup Layer2; TEE trusted execution environment is a method that relies on hardware vendors to perform calculations in isolated environments; FHE fully Homomorphic Encryption can perform calculations directly on encrypted data, but currently only supports specific operations;

'Blind computation' is a more general computing framework, as ZKP, TEE, FHE, and other encryption technologies may be part of its technical framework.

As we all know, ZKP, TEE, FHE, and other technologies are currently in the stage of exploring and optimizing the landing applications with Crypto. Blind computation may integrate these encryption core technologies and explore an integrated engineering practice solution for privacy protection.

2) The core logic of blind computation is to enhance distributed Nodes, enabling a single Node to have the ability of segmented storage and computation, combined with a verifiable open governance network, thereby achieving effective work of the Node without knowing the 'complete' data. How to understand it?

Under normal circumstances, the protection of data privacy requires storing data in ANode, then calculating it after encryption by BNode, and finally completing the storage + calculation work after decryption and verification by CNode. During this process, there is a significant cost loss in data transmission, and there is a risk of data exposure due to repeated Encrypt—>Decrypt processes. The cost of mutual trust between Nodes is also high, making it difficult to ensure that privacy is not compromised.

The business logic built by Nillion happens to compensate for this deficiency, and its general workflow is (for understanding only):

Nillion has built a distributed Node network, with each Node having enhanced storage+computational capabilities. When the Nillion network receives data transmission and processing requirements, it is first compiled and preprocessed through a specific language called Nada, which splits the original data into many segments, all of which are in encryption state.

With the help of AIVM (Artificial Intelligence Virtual Machine) to schedule and allocate, its distributed nodes will randomly store and calculate these data fragments, and finally complete aggregation and unified verification. Throughout the process, a single node cannot know all the data content, but when put together, it can complete the overall data encryption, transmission, and calculation.

Why is it said that blind calculation can aggregate applications of ZKP, TEE, FHE and other technologies? The logic is simple. In the data preprocessing stage, which is to encrypt the data, FHE (Homomorphic Encryption) technology can be fully applied. The storage and computation of data in Node can be performed in a trusted execution environment (TEE), and when aggregating and verifying the work results of Node, ZKP can be used to improve the efficiency of aggregation verification.

3. In my opinion, technologies like ZKP, TEE, FHE, MPC, etc., all have some engineering and implementation flaws. Currently, almost every track in the Crypto field is crowded with projects, but they are all working on cost and efficiency optimization, focusing on specific crypto application scenarios.

The blind calculation framework proposed by Nillion, although not yet widely implemented, may be widely adopted in the field of AI verifiable computation, machine learning, and more extensive data protection with its integrated encryption solution.