Question: Nillion decentralizes trust for high-value and sensitive data in a way similar to how blockchain decentralizes transactions. What specific gaps in existing privacy and data computation technologies motivated the creation of Nillion? Could you share some real-world scenarios where current systems fall short and how Nillion’s approach solves these challenges?
Answer: Absolutely, and thanks for the question—as well as for the invite. Nillion is what we call a blind computation network. Essentially, this means it operates as a decentralized network of nodes where none of the nodes can see the data they store or process. They are effectively “blind,” which is where the term blind computation comes from. From a technical standpoint, Nillion enables privacy-enhancing computation, allowing data to be stored and processed without ever exposing it to any party. This shifts trust away from a single entity and distributes it across the network, enhancing security and privacy.
Several existing technologies attempt to address data privacy, such as multi-party computation (MPC) and trusted execution environments (TEE). MPC allows multiple parties to compute a function over their inputs without revealing those inputs to each other. TEEs, on the other hand, rely on specialized hardware to ensure confidential computing. However, TEEs introduce a different trust model because they are typically centralized and require trust in the hardware manufacturer.
Traditionally, data is fully exposed during processing. You can encrypt data when storing it (at rest) and while transmitting it (in transit), but once it needs to be computed, it has to be decrypted. This creates a fundamental security gap. Nillion’s blind computation changes this paradigm. It enables encryption not just at rest and in transit, but also during processing—this is a game-changer. It fundamentally shifts the way we think about computation, making data privacy the default rather than an afterthought.
While blind computation can enhance privacy and security across virtually all forms of data processing, some use cases are particularly relevant today. One of the biggest areas is generative AI and data sovereignty. Currently, when we interact with AI models—whether through personal assistants, AI-driven agents, or enterprise AI tools—we have to trust a handful of powerful companies to handle our data responsibly. Every time we submit a prompt, share sensitive reports, or upload personal data, we rely on these centralized entities to not misuse our data, not share it without consent, and not sell or exploit it.
This is especially concerning when dealing with highly sensitive data like corporate reports, medical records, or genetic information. With Nillion’s blind computation, AI models could interact with personal data without ever exposing it. Imagine an AI agent that knows you better than you know yourself, yet never leaks your information or puts it at risk. You could use AI-powered analytics on confidential business data or health insights without trusting any third party with direct access.
As AI continues to evolve, the need for privacy-preserving computation will become even more critical. Nillion is leading the charge by ensuring that individuals and businesses can leverage cutting-edge technology without sacrificing their privacy or security.
Question: Let’s talk about the architectural synergy between Petnet and NilChain. The Petnet provides computation and storage capabilities, while NilChain manages shared resources and incentivization. How do these components interact to ensure seamless performance, and could you share specific examples of applications where this interaction has been pivotal?
Answer: The Petnet, as the name suggests, is focused on privacy-enhancing technology—hence the acronym “PET.” It consists of several key modules designed for different aspects of secure computation. There’s NilDB, which serves as Nillion’s decentralized database product, allowing data to be stored across multiple parties while still being queried in its encrypted form. Then we have Nil-AI, which is specifically built to support AI inference in a privacy-preserving manner. Finally, there’s Nillion-VM, which facilitates multi-party computation (MPC) for general-purpose computing, enabling users to run arbitrary computations securely using the provided SDK and native language support.
NilChain, on the other hand, functions as the blockchain layer of the network, primarily focused on payments and coordination. Any time you interact with Petnet—whether storing or processing information—you are consuming resources, and that comes with associated costs. NilChain acts as the payment layer, ensuring that users can pay for these services seamlessly while maintaining the integrity and efficiency of the system.
In simple terms, Petnet handles the privacy-focused computation and storage, while NilChain enables transactions and incentives, ensuring that the network remains sustainable and efficient. This synergy allows for a decentralized, privacy-first infrastructure that can support a wide range of applications, from secure AI processing to confidential data management.
Question: You mentioned secure storage and computation as potential use cases. In healthcare, for instance, how does Nillion support secure workflows while enabling cross-institutional collaboration on encrypted patient data? What progress have you seen in such applications?
Answer: Healthcare is a massive industry, and we’re already seeing some exciting implementations, particularly in handling highly sensitive medical data. One of the standout examples is MonadicDNA, a company working with DNA data storage and secure computation. They use Nillion to store genetic information while ensuring it can be processed privately, without ever exposing the raw data. This is a fundamental shift in how we think about handling highly sensitive health data—moving from a model where patients must trust centralized entities with their information to one where privacy is inherently built into the infrastructure.
Traditionally, companies like 23andMe have required users to trust that their data won’t be misused, shared, or exploited. MonadicDNA, by contrast, is leveraging Nillion’s privacy-preserving computation to store and process genetic information securely throughout its entire lifecycle—without exposing it to third parties.
Beyond genomics, we’re also seeing applications in health and fitness data aggregation. A company called Fulcra is building a platform that combines multiple data sources—Apple Watch metrics, calendar entries, and other health-related inputs—to uncover insights that would otherwise be impossible to detect due to data fragmentation. With traditional systems, health data is siloed across different apps and institutions, making it difficult to analyze holistically. Fulcra’s approach, powered by Nillion, enables private cross-source analysis, allowing users to discover patterns like how noise exposure impacts sleep cycles—all without compromising privacy.
Looking ahead, I believe we’ll see larger healthcare institutions adopting Nillion’s network to facilitate secure, collaborative research. Imagine multiple hospitals securely storing and analyzing patient data across institutions, identifying medical insights that would otherwise remain hidden due to data silos. Right now, medical records are rarely stored in credibly neutral, privacy-preserving environments, making collaboration difficult. Nillion offers a path toward a secure, decentralized medical data network, where institutions can share insights without ever exposing raw patient data.
The potential here is enormous. Healthcare is an industry where data sensitivity is at its peak, and we’re already seeing strong adoption on the private sector side. As privacy-preserving computation continues to prove its value, it’s only a matter of time before large institutional healthcare players leverage networks like Nillion to revolutionize data security and collaboration in medicine.
Question: About your partnership with Soarchain—this collaboration leverages NilDB to securely manage driving data while ensuring privacy. How does this reflect Nillion’s broader vision, and what lessons from this partnership can be applied to similar future projects?
Answer: Soarchain is a great example of how Nillion’s technology can be applied in real-world scenarios. As you may know, Soarchain operates in the mobility sector, collecting a wealth of sensitive driving data—things like location, acceleration, fuel consumption, and more. When you combine several of these data points, you’d be surprised how accurately someone’s destination, driving habits, or personal patterns can be inferred. That’s why privacy is critical.
Soarchain’s goal is to give users sovereignty over their own data. Traditionally, when users generate driving data, it’s controlled by a centralized entity that may sell, exploit, or monetize it without their knowledge. Soarchain flips this model by allowing users to own and monetize their data directly, creating an entirely new way for individuals to benefit from the insights their data generates.
However, this partnership goes beyond just selling data for one-time payments. By leveraging Nillion’s NilDB, Soarchain ensures that user data is not only stored securely but can also be analyzed without ever exposing the raw data itself. This opens up new possibilities—take insurance companies, for example. Instead of accessing personal driving records, insurers can analyze trends and patterns across large data sets while maintaining user privacy. Drivers could then contribute anonymized data to these analyses and earn recurring income whenever their data helps generate valuable insights.
This approach solves a major problem: how do you share and monetize data without sacrificing privacy? Nillion enables exactly that—users retain control over their data, companies get valuable insights, and privacy remains intact.
Looking at the bigger picture, this model extends far beyond mobility. Industries like healthcare, finance, and consumer research could all benefit from privacy-preserving data analytics. Instead of users having to choose between privacy and value, Nillion enables both. This is just the beginning of a broader shift toward privacy-first data ecosystems, where users, not corporations, hold the power.
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Question: Scalability is often a challenge in secure computation. How does Nillion maintain high performance as data and computational demands grow, especially in industries like AI training or IoT data analysis?
Answer: The Petnet is designed as a modular network, which means it allows users to tap into a wide range of privacy-enhancing technologies. Each of these technologies comes with trade-offs between security, cost, and performance. Some solutions prioritize stronger security, while others focus on higher speed and lower costs.
For example, cryptographic techniques like Multi-Party Computation (MPC) and Fully Homomorphic Encryption (FHE) are extremely powerful when it comes to security and trust distribution. MPC, in particular, ensures that data remains private even when processed across multiple parties—a crucial feature for use cases where security is paramount.
However, when it comes to computationally intensive tasks, such as AI training or large-scale IoT data processing, pure cryptographic solutions can sometimes struggle with performance. This is where hardware-based solutions, like Trusted Execution Environments (TEEs), come into play. TEEs can offer better performance, but they also come with different trust assumptions, since they don’t distribute security in the same way that cryptographic methods do.
The real power lies in combining these approaches. By leveraging both MPC and TEEs, for example, Nillion can balance performance and security, ensuring that computations remain private without sacrificing speed. This modular design allows organizations to customize solutions based on their specific needs—whether that means prioritizing absolute security, computational efficiency, or a balance of both.
Unlike traditional blockchains, which are often limited by fixed transactions per second (TPS), Nillion’s approach is far more flexible and scalable. It enables users to design solutions that match their specific requirements, combining different parts of the network to optimize for their unique objectives. As technology advances, these capabilities will only continue to improve.
Question: Privacy-enhancing technologies are gaining traction. How does Nillion differentiate itself from competitors like Oasis Network or Secret Network, and what are your plans to sustain your competitive edge in the long term?
Answer: Nillion represents a fundamental shift in how computation and storage are handled—especially in the context of orchestrating multiple Privacy-Enhancing Technologies (PETs). While many projects focus on one type of PET, Nillion stands out because it combines different privacy-preserving techniques to unlock entirely new applications.
One major example is in AI inference. Many AI applications require both secure storage and high-performance computation, but no single PET can efficiently handle both. On Nillion, developers can mix and match solutions like NilDB for secure storage and Nil-AI for optimized AI processing, creating a modular design space that wasn’t possible before. This flexibility allows teams to custom-build solutions instead of being forced into one rigid privacy model.
Another key differentiator is Nillion’s research-driven approach. We recently collaborated with a Meta researcher to develop an advanced AI inference technique called Discrete Wavelength Transform (DWT). This method has the potential to significantly improve AI performance using Multi-Party Computation (MPC), which could be a game-changer for both privacy and efficiency in AI workflows. This kind of continuous innovation ensures that Nillion remains on the cutting edge.
Beyond technology, distribution and community play a huge role in Nillion’s success. This isn’t just another crypto project—it’s a movement. The network has built a strong community of supporters who genuinely believe in its potential. In crypto, having the best technology isn’t enough; you need people who believe in and drive adoption. Nillion has managed to cultivate both—a powerful tech stack and a dedicated ecosystem that fuels growth.
Finally, business development is a crucial pillar. Nillion isn’t just building technology in isolation—it’s actively onboarding real-world use cases. We’re already seeing exciting implementations across various industries, from AI to healthcare to mobility, and this momentum is only increasing.
With a unique technological approach, a passionate community, and strong business development, Nillion is positioned not just to compete—but to lead in the future of privacy-enhancing computation.
