Ever wondered how different generations actually behave online?
We joke all the time about how older folks snap selfies with the camera way too close or that classic chin-up angle — and let’s be real, we can all picture that look they give.
But can you blame them? The Internet showed up during their prime, so they’ve got their own unique modus operandi for navigating it.
Sure, they might not be spilling all their secrets to Google (or whichever search engine they trust), but trust me, they’re still out there scrolling, searching, and exploring in their own way.
The newer generation, on the other hand — now that’s where it gets interesting. You see, I sort of researched what the classic behavior of a younger generation of Internet users is, especially because it relates to sharing stuff online — I mean, private stuff, level-3 data, high-value data.
In my head, I thought, surely this generation must be more careful, a bit more private than the older crowd. After all, they're "Internet-savvy" and grew up just as familiar with the advantages and drawbacks of the Internet as a toddler is with Cocomelon.
Well, breaking news, and without exempting myself, we are nowhere near being actionably private. We overshare — a lot!
Why? That’s the next logical question to ask! Despite understanding how our data can be used with mal-intent, why do we still share as much as we do, a lot more than the older generation?
You see, in this version of the Internet where the algorithm is king and content is hyper-tailored to users, navigating the need to be private and harnessing the Internet for our needs is a dilemma in which we somehow have to settle for a trade-off.
So what do we do? We click through those options the moment we sign up, hit “accept” on the cookie prompts, fill out forms, like, repost, query, update — and, bit by bit, hand over data that third parties can use however they please.
What’s even funnier? The consequences of our online behavior aren’t some distant future problem — they’re here, right now, in the form of AI.
Just like humanity’s endless appetite, AI is hungry for more and more data, feeding off everything we’ve shared and continue to share online.
Now, don’t get me wrong — I’m not here to scare anyone. This isn’t necessarily a bad thing, but it’s not exactly ideal, either. While AI is incredible, and personalized AI is even more interesting, it needs really private data, level-3 stuff, to function at its best possible level.
The bottom line? Maybe it’s time to rethink the trade-off.
Perhaps we need a new kind of privacy — a foundation for data security where apps and users can coexist on trust, not cookies.
I know what you’re thinking: What’s the catch, Ollie?
The catch? If you’ve been keeping up with what we publish, you already know we’ve shared “the catch” before. But if you insist, go ahead and check out our recent article on Nillion.
This article isn’t about the catch itself. Instead, we are leaning more into it. We want to dissect this new type of privacy called “blind computation.”
Over the next few minutes, we’ll be digging our feet into what blind computation is, deeper than the previous article. We will examine why it matters, how it works, and why Nillion is leading the charge. We’ll explore why developers, apps, and users alike should care. We’ll also dive into Nillion’s latest updates and journey so far.
What is blind computation?
Blind computation is a complete redefinition of privacy from the ground up. It’s a new approach where cryptographic techniques are combined to ultimately achieve a blind computer, wherein data is masked (data masking), and information is secured by splitting it into separate parts.
These masked pieces are sent to multiple nodes for processing, then reassembled and decrypted, revealing the content only to the authorized recipient.
This concept sets the foundation for a more secure system for end-to-end data transfer, allowing applications and users to benefit from improved data security and enhanced trust.
Nillion’s application of this new type of privacy is what’s interesting, though. Nillion’s Blind Computer (NBC) functions as a public decentralized network that allows applications to build on top of it.
It takes advantage of its private storage infrastructure and secure data computing capabilities to offer users a more trusted environment to interact with apps built or integrated with the network and vice versa.
Take AI chatbots, for example — they’re pretty cool, right? They’ve got answers to almost everything and can even act like unpaid therapists. But sharing personal information with an AI? Not everyone’s comfortable with that.
Nillion’s Blind Computer (NBC)
Nillion’s Blind Computer solves this. AI chatbots built on Nillion can use encrypted or private user inputs without actually seeing the content.
Plus, they can protect their model’s integrity by leveraging Nillion’s private storage infrastructure.
This means you get the support you need, and your privacy stays intact.
NBC combines several privacy-enhancing technologies (PETs) such as fully homomorphic encryption (FHE), differential privacy (DP), garbled circuits (GC), linear secret-sharing schemes (LSSS), trusted execution environments (TEE), oblivious transfer (OT), functional encryption (FE), and multi-party computation (MPC) to achieve secure data privacy.
However, it’s important to note that Nillion’s approach to MPC is unique: It extends linear secret-sharing schemes (LSSS) to handle complex operations like evaluating sums of products on hidden inputs without leaking information.
It also supports asynchronous computation, aligning non-interactive arithmetic data masking with asynchronous workflows — no message exchanges are needed.
In simpler terms, MPC is all about teamwork — nodes in a network crunching numbers together without anyone spilling the beans ‘bout their own data.
Nillion takes this up a notch with LSSS, a fancy way of chopping data into pieces and sharing them among nodes. The twist? Each node only gets a part of the picture, so no one knows the whole story.
Here’s where it gets cool: Nillion’s magic lets these nodes tackle complex math, like summing up products of those secret pieces, all without peeking at the original data.
With asynchronous computation, nodes can work independently — solving their piece of the puzzle without needing to constantly check in with others. It’s like teamwork, but quieter and way more secure.
Although Nillion uses a combination of PETs, there is a focus on MPC due to its cryptography-based nature. To know more about this, check out the Nillion docs.
On paper, all of this sounds great and pretty exciting. However, for it to truly be veritable, applications have to want to take advantage of the Nillion network, right?
Based on this, it is important to examine the progress of Nillion’s blind computation as a growing category in the crypto space's privacy sector.
Blind computation as a category
Privacy is a big concern in crypto. With several protocols building independently to solve fundamental issues leveraging the blockchain, privacy through blind computation becomes the melting pot.
In this regard, blind computation is a category that encapsulates several sectors in the crypto space.
In artificial intelligence (AI), blind computation is needed to provide secure model training, private large language models (LLM) inference, retrieval augmented generation, and synthetic data generation.
Nillion takes this a step further by developing AIVM, a platform that ensures that users (personal, business, researchers) can interact with LLMs in a safe and private manner.
In the healthcare industry or decentralized science (DeSci) sector, blind computation is needed to enhance secure collaborative drug discoveries and research breakthroughs and ensure the safety of healthcare data.
That’s not all. When we merge blind computation and DeFi, we have blind DeFi, a morphed sector that will see private on-chain DeFi primitives such as private vaults, private perps, and optimal auctions thrive.
Protocols will be able to protect yield strategies or develop private AI agents that execute yield strategies — all built on Nillion’s blind computer. If that doesn’t send shock waves through your body, well, I don’t know what will.
There are also industries, like social systems, where blind computation will become useful for credit scoring. Alongside, in the identity industry it will help foster improved decentralized KYC systems, single sign-on (SSO), and efficient data provenance.
NBC will unlock all of these industries, becoming a unifying category — it is, in fact, the true missing piece in crypto.
So, who is building on Nillion and what are they building?
The real question is, having read all of this promising stuff that the Nillion network will bring about, what will you choose to build on Nillion?
While you’re still deciding what to build, here are some horizontal and vertical applications and protocols ready to use Nillion’s blind computation network.
On the layer-1 [L1] side of things, Aptos, Near, and SEI are all teaming up and integrating Nillion to bring its blind computation tech to their ecosystem.
These partnerships or integrations will allow natively built apps to take advantage of Nillion’s blind computing capabilities to provide better privacy for end users.
Layer-2 [L2] networks are also not left out — Arbitrum and Mantle have joined the NBC ecosystem with similar goals to the L1s mentioned above.
On the healthcare front, projects like Agerate, which is building an at-home blood test and mobile app that helps users track their aging in real-time, have integrated with NBC to safeguard users' medical records.
On the AI scene, Ritual, Rainfall, Mizu, Virtuals, and loads of other AI apps and agents are deploying on the Nillion network.
The Nillion ecosystem is growing. While some are integrating with the Nillion network, others are using the Nada DSL to build blind apps from the ground up.
How is Nillion different from other privacy protocols?
One obvious way Nillion’s blind computation sets itself apart from the competition is its ability to combine several PETs. That lets it cater to a wide range of use cases based on the needs of the applications.
Nillion also takes a novel approach in several areas, including the implementation of these technologies, ensuring that the network is unique with tailored abilities.
Additionally, Nillion uses a foundational approach to redefine privacy, providing developers with a new language, SDK frameworks, and technical support to build blind applications on the network.
Nillion stands out by prioritizing cost efficiency and customization for its network participants. Nodes can operate in clusters, allowing operators to take a modular approach to their setups.
This flexibility enables participants to choose options ranging from cost-effective infrastructure to enhanced security by strategically placing node clusters across geolocations within the network.
Nillion ecosystem update
Before diving into the latest updates from the Nillion ecosystem, I have to say — Proof of Tuk Tuk 🛺 is one of the coolest marketing ploys in crypto last year, hands down! The Nillion team absolutely crushed it with this one.
At last year’s Devcon held in Bangkok, Nillion conducted a hackathon event in which many developers — using the NBC SDK — developed blind applications and integrated them with Nillion’s encrypted secret storage, competing for a $10k split price.
The event also saw Nillion host a dev workshop as a side event, during which developers were taught how to quickly build a blind app on Nillion.
Additionally, the Nillion verifier program is still active — open to anyone who wants to participate. Since the program’s inception, over 420k verifiers have gone live, with nearly 200 million secrets verified and nearly one terabyte of data secured.
Nillion has also released an updated version of its SDK tagged Nillion SDK v0.7.0. The updated version comes along with it: client-ts with web + node.js support, Nillion-client with integrated payments, open source of Nada-DAL, and added support for expanded boolean operations.
Another important update is Nillion’s latest funding round. The project recently announced an additional $25 million VC round led by Hack VC, bringing the total amount raised to $50 million.
Final thoughts
Here’s where I lay it all out: I think Nillion is terrific. Not only is what the protocol building incredibly promising, but it’s also proving that it works.
In today’s crypto condo, where so many projects are stuck in the weeds, only a few are building real value or pioneering game-changing concepts and technologies that could redefine the industry.
Nillion is one of them, offering the missing piece: a new type of privacy. In fact, projects like Nillion must succeed for our industry’s long-term success.
On a personal note, beyond the groundbreaking tech, Nillion reminds me of Coinbase’s stellar crypto evangelist marketing.
The messaging is intentional, polished, and backed by substance. With such a strong foundation, Nillion’s potential is sky-high — if it works, it could truly reshape society as we know it.
Nillion’s mainnet is slated to go live next month, i.e. in February 2025.
Right now, the PETNET testnet is live, and everyone is invited to participate. You can even join the Nillion community round by taking the ‘NillPill’ and supporting the project at the same Series A valuation as VCs. But, of course, that decision is yours to make.
