On Privacy and Data

“Privacy and Data,

Data and Privacy,

Oh no another breach,

Another problem…”

Companies need data—it's their main source of revenue, their main product, their main goal. But being the custodian of data is also a big responsibility, as Peter Parker's uncle said: with a big data set comes great responsibility. And God, if we try, tech industry folks—and especially OpSec professionals—know how hard it is to defend a company, no matter its size, from being compromised. Users' data is a honeypot for hackers, with the global average cost of a data breach hitting $4.44 million in 2025—a 9% drop from the prior year's record high, thanks in part to better AI-driven detection.

Yet breaches keep coming: over 4,100 were publicly disclosed last year alone, and 2025 has already seen massive incidents like the Chinese Surveillance Network exposure of 4 billion records. And nowadays, more than ever, our AI convos reveal more about ourselves than we would like—especially risky when 1 in 6 breaches this year involved AI-driven attacks, and 84% of people cite cybersecurity as their top AI concern. AI has become an ally, but also a liability if it's not treated right, with 40% of organizations facing AI-related privacy incidents.

This is a problem, yes, but one where really smart folks have been working for a while. Cryptography and hardware security help mitigate the problem. Let's discuss, then, which are these methods, and why we rely on them, and when.

Cryptography: ZK, FHE and other spells

For people outside the tech industry, or those who haven't seen the backend of a serious operation, cryptography seems like complicated magic that nerds invoke when discussing their latest paranoid discovery. But no—cryptography is a science, and like every science, sometimes it can feel like magic.

Cryptography helps us protect our data, and pretty much every app we use has applied it to some extent to safeguard its operations. Whether it's a simple encryption method or a messaging app system aiming to be unbreakable (like Signal or Telegram), these algorithms follow us around in daily life without people even knowing how important they are. Yet, vulnerabilities persist: a staggering 92% of mobile apps in 2025 were found to use insecure cryptographic methods, leaving millions exposed to data risks. These methods use advanced computer science techniques to hide information or make it unavailable to undesirable players (hackers).

Nowadays, some techniques are gaining a lot of popularity—things like Zero-Knowledge Proofs (ZK) or Fully Homomorphic Encryption (FHE) are cryptographic methods to secure information, and there are others. For instance, the ZK-proving market is projected to reach $97 million by the end of 2025, driven by blockchain adoption and NIST's push to standardize ZKPs this year. Meanwhile, the FHE market is valued at around $235 million in 2025, with experts forecasting growth to $352 million by 2030 as privacy demands surge in cloud computing. Another standout "spell" is Multi-Party Computation (MPC), which lets multiple parties jointly compute on private data without anyone revealing their inputs—think of it as a group spellcasting session where secrets stay hidden. MPC is exploding in use for collaborative scenarios, with its market expected to hit $1.2 billion by 2025, fueled by applications in secure AI training and financial data sharing where trust is low. It's often combined with ZK or FHE for hybrid strength, making it a go-to for industries like healthcare, where 45% of organizations report adopting MPC for privacy-preserving analytics this year. This magic ensures our communications and files stay safe, protecting our privacy.

Hardware Solution: TEEs, MCPs and Other stuff

But besides all these methods, you have other approaches—not based merely on software, but also fighting from the hardware. Distributing computing, creating safe enclaves, and other techniques rely on transforming the way the infrastructure is built to present solutions. These methods tend to be more robust against certain attacks because they leverage physical hardware protections rather than just code. A prime example is Trusted Execution Environments (TEEs), which are isolated areas within a processor (like Intel SGX, ARM TrustZone, or AMD SEV) that run code and handle data securely, even if the surrounding system is compromised. TEEs work by creating "enclaves" where sensitive operations occur—think of them as fortified bunkers inside your computer's brain, shielding against malware, insiders, or even the OS itself. They're being used in everything from cloud computing (e.g., AWS Nitro Enclaves for secure data processing) to mobile devices (e.g., Apple's Secure Enclave for biometrics like Face ID).

In AI contexts, TEEs enable confidential inference, where models process user data without exposure, which is crucial as AI adoption surges. The confidential computing market—largely driven by TEEs—is projected to reach $17-24 billion in 2025, growing at a CAGR of 46.4% through 2032, with over 75 billion IoT devices expected to benefit from such hardware security by mid-decade. Adoption is booming, with enterprises reporting up to 50% market share in North America for TEE-enabled solutions in high-stakes sectors like finance and healthcare. Hybrid approaches blend these hardware foundations with software cryptography to tackle complex privacy challenges.

For instance, pairing TEEs with Multi-Party Computation (MPC) allows distributed AI training where data stays private across parties, with the TEE handling secure execution to boost efficiency and resist side-channel attacks. Similarly, combining TEEs with Fully Homomorphic Encryption (FHE) enables computations on encrypted data inside the enclave, reducing overhead—think healthcare apps analyzing patient records without decryption risks. Or, integrating Zero-Knowledge Proofs (ZK) in a TEE verifies AI outputs without revealing models, as seen in secure cloud analytics. These hybrids are gaining traction in 2025, with reports showing 45% of organizations adopting combined hardware-software PETs (Privacy-Enhancing Technologies) for better scalability and quantum resistance.

By merging the tamper-proof nature of hardware with the flexibility of crypto spells, hybrids provide a "best of both worlds" defense, making them ideal for data-intensive industries where pure software falls short.

Blockchain Understands

Whether you understand that the world of crypto isn't just about speculative internet money but is rooted in real technological innovation or not, you know that a lot of the buzz around crypto projects surrounds privacy. ZK Layer 2s, Monero, Zcash, Phala Network, Oasis Protocol... and so on. A lot of projects in the crypto space try to push the boundaries of easy usage for these kinds of technologies. And they mix both approaches—blending cryptographic software like ZK proofs and FHE with hardware like TEEs for decentralized, tamper-proof privacy.

These blockchain-based solutions leverage distributed ledgers to enable secure, verifiable data handling without central authorities, making them ideal for privacy in a trustless world. For instance, ZK Layer 2s (like zkSync or Aztec) use zero-knowledge proofs on scaling layers to bundle transactions off the main chain, ensuring privacy and efficiency—recent trends show these surging due to strong demand for privacy-enhanced scaling amid regulatory pressures. ROFL, for example, is one of the latest products from Oasis Protocol—it uses TEEs running on their decentralized infrastructure to execute programs privately, enabling complex tasks like AI computations off-chain while verifying results on-chain with blockchain-level trust. Recent updates highlight its role in boosting developer activity and confidential app building. And well... among all these fuzzy keywords, you might be thinking: Ok, but why on earth would I use this? It doesn't only sound complicated, but it is complicated.

Well, in a world where data is the oil of the economy, protecting it is one of the most important things you have to do. It's your competitive edge, and your users rely on you to do so—not only that, governments hold you responsible for everything, so you better watch out. We rely on them because they decentralize trust—no single point of failure like in traditional clouds—while providing verifiable privacy at scale. Use them when building apps needing anonymous transactions, scalable privacy layers, or confidential AI, especially in regulated sectors like finance or healthcare where data breaches pose major risks. Taking advantage of blockchain technology and advancements in privacy methods seems potentially key for every player trying to upgrade their stack without having to create all these things from scratch.

So what this means?

So, what does this all add up to? Let's dive into the practical side—potential applications of these blockchain-powered privacy solutions. We're talking about real-world ways to harness ZK Layer 2s, hybrid TEE-crypto setups like ROFL on Oasis, and projects such as Phala or zkSync to build systems that keep data safe while unlocking innovation. The beauty here is decentralization: no big tech gatekeepers holding all the keys, just verifiable, private flows that users and businesses can trust. And when we zoom in on AI, these methods shine for creating secure data pipelines—think end-to-end processes where raw data stays encrypted, computations happen privately, and outputs are auditable without leaks.

In finance, for example, blockchain privacy tools enable anonymous yet compliant transactions. Monero's ring signatures or Zcash's shielded pools let users trade or lend without exposing identities, while ZK Layer 2s scale this for high-volume DeFi apps—reducing gas fees and hiding details from prying eyes. This extends to supply chain tracking, where companies use Phala's TEE-based compute to verify product origins privately, ensuring competitors don't snoop on proprietary data. But the real game-changer is in AI secure data pipelines. These methods allow for training and deploying AI models without centralizing sensitive info, addressing the explosion of data breaches in AI workflows. For instance, federated learning pipelines—where devices collaborate on model updates without sharing raw data—get a privacy boost from blockchain hybrids.

Projects like Beyond Tech build modular infrastructure that turns complex data into verifiable AI insights, using ZK proofs and TEEs for privacy-preserving pipelines in decentralized compute. Similarly, Oro's TEEs combined with blockchain logging process AI data uploads securely, preventing leaks in enterprise setups—no raw data exposure, just trusted results. In healthcare, Balkeum Labs leverages federated learning on blockchain for privacy-preserving AI, building real-world data pipelines that comply with regs like HIPAA while enabling collaborative research. Frameworks like the Privacy Preserving Federated Blockchain Explainable AI Optimization (PPFBXAIO) integrate ZK, MPC, and blockchain to optimize AI models across parties, ensuring explainability and privacy in sectors like predictive analytics. Even in general AI agents, decentralized guardians merge lightweight AI with blockchain to shift privacy control to users, creating verifiable pipelines for tasks like personalized recommendations without big data silos. Using only these methods—cryptography (ZK, FHE, MPC), hardware (TEEs), and blockchain hybrids—you can craft AI pipelines that are secure by design. Start with data ingestion: Encrypt inputs with FHE or MPC, process in TEE enclaves via ROFL for off-chain efficiency, verify with ZK proofs on-chain, and output auditable insights. This setup handles everything from anomaly detection in security (AI spotting fraud without raw logs) to collaborative training (multiple orgs building models via federated blockchain).

It's especially potent in 2025, with AI-native pipelines using layer-2 scaling for faster, cheaper privacy. The result? Pipelines that decentralize trust, automate compliance, and scale without sacrificing speed—perfect for devs upgrading stacks without reinventing the wheel. In short, these applications aren't sci-fi; they're here, turning privacy from a headache into a superpower for AI-driven worlds.