With Nillion’s mainnet launch on the horizon, I recently took a closer look at the origins of the concept of Blind Computation and how it relates to other relevant notions in privacy computing, such as MPC and FHE. Blind Computation first appeared largely in the context of Blind Quantum Computation, originating from quantum computing research. It can be traced back to papers from around 2001 to 2003 (https://arxiv.org/abs/quant-ph/0309152). Its definition is: performing processing and computation on data without revealing the data’s contents. In other words, the party executing the computation remains “completely unaware” of the data they are handling. The core of this concept is that inputs, outputs, and even the computational logic stay confidential throughout the process and are not disclosed to the party performing the computation. It sounds quite similar to FHE (Fully Homomorphic Encryption), and indeed, they address a similar problem space. However, Blind Computation does not have a single, uniquely formalized definition in cryptography; sometimes it’s used in a broad, conceptual sense. Meanwhile, FHE is a very specific cryptographic tool or framework that allows for arithmetic operations (addition, multiplication, or any circuit computation) on ciphertexts without decryption, and later lets you decrypt the result to get the same outcome as if you had performed the operations on the plaintext directly. So from the vantage point of “Blind Computation” as a broad concept, it is actually implemented using a variety of specific cryptographic approaches, which may include MPC, ZKP, TEE, or FHE. In short, Blind Computation is an overarching term. Nillion’s approach to Blind Computation aims to achieve privacy-preserving computation and storage in multi-node distributed scenarios, using secret sharing, MPC, TEE, and other tools to ensure that each node cannot see the plaintext data locally, yet they can still work together to complete the required computation. Its design adopts a two-layer architecture, split into a privacy computation layer (“Petnet”) and a coordination layer(“nilChain”). Within this architecture, Nillion has also defined a set of Blind Modules that serve as the core components for executing specific functions. • Petnet (Privacy Computing Layer): A distributed network of nodes responsible for actual data storage and computation. Petnet leverages various privacy-enhancing technologies (PETs) to ensure that data remains in encrypted or secret-shared form during processing. Inside Petnet, nodes are dynamically organized into clusters to perform specific tasks; each cluster can be viewed as an independent blind computation unit that enables data sharding across multiple nodes. • nilChain (Coordination Layer): Built on the Cosmos SDK, this blockchain handles global resource management and incentives. nilChain itself does not process any private data or compute logic; rather, it acts like an “operating system” for the network, dealing with task scheduling, node management, and payment settlement. For instance, nilChain tracks node staking and reputation, assigns computation tasks to suitable Petnet node clusters, and manages user payments and node rewards. In theory, the blind computation capabilities provided by Nillion can be applied to a variety of scenarios requiring data privacy, such as privacy-enhanced AI applications, privacy DeFi and transactions, and encrypted data analysis. However, to realize its vision, Nillion must make breakthroughs in security, performance, and ease of use—three areas that each present well-known challenges in the industry. For instance, classical MPC faces the issue of communication complexity, FHE suffers from heavy computational overhead, and TEE relies on the trustworthiness of its hardware base. Nillion is taking a combined, innovative approach that may theoretically allow these methods to complement one another’s shortcomings, but also introduces overlapping technical risks. Demonstrating that its protocols are reliably secure and highly efficient under real-world conditions will be a critical challenge for the Nillion team going forward. Most importantly, once the mainnet goes live, the question becomes whether developers will be able to build meaningful, widely adopted privacy-focused applications on top of it.