Imagine an Ethereum DeFi team building an AI-powered credit scoring protocol in early 2026. They hit a familiar wall. This model relies on sensitive financial and behavioral data, inputs that cannot be safely touched on public blockchains without exposing users or triggering regulatory scrutiny. Traditional workarounds are equally well known. Teams push sensitive logic to a centralized off-chain server. Doing so reintroduces single points of failure, trust assumptions, and a larger attack surface, undermining the decentralization you were trying to achieve.
This irony has defined much of Web3’s past decade. Public blockchains are great for transparent finance, but have a hard time supporting Applications where privacy is not an option, such as healthcare, enterprise workflows, identity systems, and AI trained on proprietary data. As the Ethereum ecosystem expands, tensions will only increase. With hundreds of billions of dollars of value locked across DeFi, NFTs, and tokenized assets, Ethereum has become the moderating layer for on-chain activity. But when it comes to private computing, where data must remain confidential even during processing, developers still face a difficult choice between sacrificing decentralization and sacrificing privacy.
It is this gap that Nillion aims to fill after migrating from Cosmos to Ethereum. The move, completed in early February, brings Nirion’s Blind Computer, a decentralized network for private computing and storage, directly into Ethereum’s orbit. The transition includes migrating the $NIL token to ERC-20, launching an Ethereum-based reconciliation layer, and debuting Blacklight, a decentralized verification system designed to continuously audit private computations. The bet is ambitious. Verifiable privacy will become an inherent property of Ethereum, potentially unlocking new categories of applications without reintroducing trusted intermediaries.
Ethereum’s privacy paradox
Ethereum’s core design makes no secret of its tradeoffs. Transactions are public by default, execution is transparent, and validators can independently verify all state transitions. Although this architecture supports trustlessness, it also makes handling sensitive data extremely difficult. Applications that need to process private inputs, from credit history to medical records, are at risk of information leakage if they rely solely on on-chain execution.
As a result, many Ethereum applications rely on a hybrid model. Sensitive data is pushed to a centralized database or trusted execution environment, and the public chain handles settlement and reconciliation. These approaches may work, but they compromise composability and quietly reintroduce the assumption of trust. Nillion CEO John Woods said in an interview that this is a structural limitation rather than a tool gap.
“Ethereum is very good at coordination and validation,” Woods said. “What has historically been a struggle is a practical infrastructure for storing and running private data. Since everything on Ethereum is observable by default, developers will end up moving their most sensitive logic off-chain and asking users to trust them.”
This exposure stems from Ethereum’s public ledger, where transactions and smart contract state changes are visible by design, allowing anonymization through transaction graph analysis. Researchers note that Ethereum’s account-based model and rich stateful contracts pose additional privacy challenges that prevent applications containing sensitive data from being used. Over time, Woods argued, this dynamic erodes real decentralization. He said, “If private enforcement cannot be continuously verified, trust will inevitably revert to the operator rather than the network.”
Regulatory pressures exacerbate the problem. european data protection rules Emerging AI governance frameworks also require stronger assurances around data processing, auditability, and user consent. For developers, cost is not only technical complexity but also strategic risk. Rebuilding the off-chain infrastructure undermines the very network effects that make Ethereum attractive in the first place.
Crowded privacy environment
Nirion is not alone in trying to solve this problem. Over the past few years, privacy-focused projects have considered a variety of cryptographic and hardware-based approaches, each with its own tradeoffs.
Zero-knowledge systems, like those used by Aztec and Polygon Miden, rely on mathematical proofs to validate private transactions without revealing the underlying data. While these techniques have enabled shielded transfers and private voting, they remain expensive and complex for compute-intensive workloads such as machine learning inference and large-scale analytics.
Other networks place more emphasis on a trusted execution environment. Projects like Phala Network and Secret Network run secret smart contracts inside secure hardware. Although this model improves performance, it has attracted criticism because it relies on relatively passive trust assumptions. High-profile vulnerabilities in enclave hardware highlight the risks of assuming that an environment, once proven, is secure indefinitely.
Hybrid approaches are also emerging. Data availability layers such as Arweave and restaking frameworks such as EigenLayer are exploring new ways to extend trust and verification across networks. On the other hand, a multiparty computation protocol such as threshold network Allows computation of encrypted data shared between multiple operators. A common challenge with these systems is fragmentation. There is no single approach that clearly addresses private storage, execution, and validation at scale.
This fragmentation helps explain why the Ethereum Foundation’s Privacy Roadmap emphasizes making privacy a first-class property of the ecosystem rather than an optional add-on. To support digital commerce, identity, and the transfer of value without exposing users, private computing must be seamless and verifiable.
Nillion’s defense-in-depth model
Nillion’s architecture seeks to combine these strands into a more composable system. At its core, it is a blind computer, allowing the data processed inside to remain encrypted. Reliable execution by hardware environment. Unlike purely zero-knowledge-based systems, this design supports compute-intensive workloads with low latency. Unlike many TEE networks, Nillion overlays hardware assumptions with additional cryptographic techniques such as secure multiparty computation and homomorphic encryption to reduce reliance on a single trust anchor.
The most distinctive component is the black light. Rather than treating enclave attestation as a one-time event, Blacklight introduces a distributed network of independent verifiers that continuously checks whether private workloads are running the expected code on uncompromised hardware. These Blacklight nodes do not access user data or execution logic, only verify encryption certificates.
Woods said the change was driven by lessons learned from deploying private computing in production environments. “Civilian calculations are only useful if they are still verifiable after launch,” he said. “If an integrity check is done once and never checked again, you are effectively trusting that nothing will go wrong. Blacklight turns that assumption into something that the network can monitor and enforce over time.”
From Nillion’s perspective, the move to Ethereum is more about working together than abandoning one ecosystem for another. Ethereum offers global payments, configurability, and a large developer base. Layer 2 of Nillion serves as the coordination and economic layer where $NIL is staked, verification work is assigned, and rewards are distributed. Blind Computer runs private workloads, and Blacklight ensures that those workloads are verifiable long after deployment.
Prior to the transition, Nillion’s network was already experiencing significant usage, serving over 111,000 users, storing over 635 million private records, and executing over 1.4 million private inference calls. Proponents argue that this traction indicates real demand for civilian computation, rather than purely theoretical interest.
Initial Application and Adoption Indications
That demand is seen in the variety of applications already built on the platform. Private AI tools like nilGPT process user prompts without exposing sensitive context. Data ownership platforms like Rainfall allow individuals to monetize the insights gained from their data without handing over their raw records. Health-focused projects like HealthBlocks and MonadicDNA use blind computers to analyze wearable and genomic data in encrypted form. Consumer-facing experiments like Puffpaw’s “vape-to-earn” model rely on processing private data to align incentives without compromising user privacy.
Taken together, these applications suggest broader changes. Privacy-protecting infrastructure does more than just hide transactions. This enables a whole new category of software that would be impractical or legally unsustainable in a completely transparent system.
The road ahead
Despite the early traction, skepticism remains valid. Scaling decentralized verification networks will not be easy, and as private computing expands into healthcare and finance, regulatory oversight is likely to increase. The durability of TEE-based systems continues to be tested and sufficient incentives are needed to attract diverse node operators.
The challenge for Woods is balance. “The goal is not to make Ethereum private by default,” he said. “It’s about making privacy verifiable where privacy is claimed. Without it, applications either remain exposed or quietly revert to trusted infrastructure.”
the scenery echo the discussion It was created by Ethereum co-founder Vitalik Buterin. He believes verifiable privacy is essential to enable sensitive applications without widespread surveillance. Taken together, these perspectives suggest that privacy is not an escape from transparency, but a prerequisite for Ethereum’s next phase.
For now, Nillion’s transition highlights broader changes within the ecosystem. Privacy is no longer treated as a niche feature or an optional add-on. As AI, enterprise adoption, and regulatory compliance converge, the ability to compute without exposing sensitive data may become a fundamental requirement. Whether Nillion ultimately emerges as a dominant layer or one of several competing solutions, its approach highlights a growing consensus. Ethereum’s future may depend as much on closing its privacy blind spots as it does on scaling up transactions.
