Privacy has always been one of crypto’s most debated features, and one of its most misunderstood. On one side, public blockchains are radically transparent: anyone can inspect transactions, wallet activity, and on-chain behavior in real time. On the other hand, most real-world financial activity depends on confidentiality, such as payroll, trade execution, personal spending, business treasury management, identity checks, and now AI training data and model outputs.
That tension is exactly why privacy is such a trending topic in the blockchain industry. The sector is now bigger than just “privacy coins,” as it’s become an evolving toolkit of cryptography, network design, and verification methods that help users and applications prove what’s true without exposing everything.
What “Privacy” Means in Blockchain
Most blockchains are pseudonymous, not private. A wallet address doesn’t automatically reveal your name—but once an address is connected to your identity (exchange deposits, KYC rails, doxxed ENS names, reused addresses, social posts, etc.), the chain becomes a permanent activity feed.
In practice, on-chain privacy can refer to several different goals:
- Sender privacy: hiding who initiated a transaction
- Receiver privacy: hiding who received funds or data
- Amount privacy: hiding how much was transferred
- Activity privacy: hiding what you did (e.g., which dApp you used, which position you opened)
- Data privacy: keeping sensitive inputs private while still enabling computation
Not every system offers every kind of privacy. Some networks provide default privacy, others provide optional privacy, and many systems focus on privacy at the application layer rather than the base chain.
The Building Blocks of Privacy Crypto
Privacy in Web3 typically comes from a handful of cryptographic and architectural approaches:
1) Ring signatures + confidential transactions – These techniques blur “who signed” and can also obscure amounts. Monero is the most well-known example of combining ring signatures with confidential transaction methods to hide sender/amount details.
2) Stealth addresses – Stealth addresses generate one-time destination addresses so observers can’t easily link a recipient’s public address to incoming payments. Monero uses this approach as part of its privacy design.
3) Zero-knowledge proofs (ZKPs) – ZKPs let you prove a statement is true without revealing the underlying data. They enable transactions where validity is proven without publicly exposing details. Zcash is the canonical example of a system built around ZK proofs for shielded transactions.
4) Mixing / CoinJoin-style coordination – Some systems provide optional privacy by combining multiple users’ transactions so it’s harder to trace flows. Dash’s PrivateSend is often described in this category as an optional CoinJoin-style mechanism rather than “always-on” privacy.
5) Privacy-preserving computation – Beyond payments, modern privacy tech increasingly focuses on computation: proving results, verifying models, validating identity, and enabling collaboration without exposing raw inputs.
Where do Privacy Coins Fit?
Privacy coins are the most visible part of the sector because they represent a direct consumer use case: private digital cash. But their designs differ sharply and those differences are significant.
Monero (XMR): Privacy by Default
Monero is designed so that privacy isn’t a toggle but the baseline. It emphasizes transaction confidentiality through mechanisms widely described as ring signatures, stealth addresses, and confidential transaction techniques (RingCT).
In the privacy-crypto landscape, Monero often represents the “strong privacy as a principle” end of the spectrum because the system’s default behavior is to reduce traceability for typical transfers. That design has benefits for personal privacy and fungibility, but it also draws regulatory pressure and exchange scrutiny in some jurisdictions.
Zcash (ZEC): Optional Privacy via ZK Proofs
Zcash is known for bringing zero-knowledge proofs into mainstream crypto through shielded transactions (commonly associated with zk-SNARKs). In Zcash, privacy can be optional depending on whether users use transparent vs shielded addresses/transactions.
In the blockchain space, Zcash is often viewed as a “privacy technology showcase” as much as a currency because it demonstrates how ZK systems can prove correctness without revealing sensitive details.
Dash (DASH): Optional Privacy Features
Dash is sometimes grouped into privacy lists because it offers optional privacy functionality (often described as CoinJoin-style mixing via PrivateSend). However, the privacy characteristics, threat model, and “default vs optional” stance differ from systems like Monero. This is a good reminder: “privacy coin” is a category label, not a guarantee of identical privacy properties.
Depending on how you define the sector, you’ll also see privacy-oriented networks and app layers focused on private smart contracts, private data sharing, and privacy for DeFi rather than “private payments.” Many modern efforts (including ZK-focused app stacks) are effectively building privacy primitives that can be used by multiple applications instead of a single “privacy currency.”
The Next Phase: Privacy-preserving Verification for AI and On-chain Systems
As AI becomes embedded into Web3 via trading agents, gaming agents, identity agents, and automated governance, the stakes of functional privacy go up. Models may rely on private datasets, users may need to prove identity attributes without revealing identity, and systems may need to prove that an AI output is correctly derived without exposing training data, prompts, or proprietary weights.
This is where concepts like verifiable AI and ZKML start to matter: proving AI outputs are trustworthy and reproducible under a defined process, while keeping sensitive information private. ARPA Network has been positioning itself specifically in this direction.
How ARPA Network Contributes to the Privacy Space
ARPA Network functions as a decentralized cryptographic infrastructure focused on fairness, security, and privacy. It is built around threshold cryptography and verification-first design. Two pillars are especially relevant to privacy preservation and verifiable AI:
1) Randcast: Verifiable Randomness as a Fairness Primitive
Randomness is a subtle privacy-adjacent ingredient in many systems: gaming outcomes, NFT allocations, lotteries, and any mechanism where predictability or manipulation can break fairness.
ARPA’s Randcast is positioned as an on-chain verifiable random number generator that developers can integrate via SDKs/APIs to produce randomness that can be verified on-chain.
This results in randomness that can be verified, which reduces reliance on centralized or opaque sources and strengthens trust in open systems.
2) Verifiable AI: ZK-powered Trust + Privacy for Machine Learning Outputs
ARPA’s Verifiable AI initiative is explicitly framed around combining zero-knowledge proofs with machine learning so that outputs can be independently verified without exposing confidential data.
ARPA has also publicly announced a verifiable AI framework using ZK proofs, positioning it as a way to address data integrity, model transparency, and verifiability. The endeavor is particularly relevant as AI technology continues to integrate with blockchain and high-stakes automation.
ARPA has always emphasized privacy-preserving technology as foundational for an AI-driven internet, aligning privacy with verifiability rather than treating them as opposites.
While the aforementioned privacy coins focus on private value transfer, ARPA is leaning into private + verifiable computation. The latter will become increasingly important as AI agents start making decisions that move money, allocate resources, or influence outcomes.
Conclusion
Privacy in the blockchain space evolved because people need room to transact, build, and participate without turning every action into a permanent public broadcast. What’s changing now is the shape of the privacy problem. It’s no longer only about hiding balances or masking transaction paths. It’s increasingly about protecting sensitive data while still being able to prove correctness, fairness, and integrity. This is especially true as AI becomes embedded in Web3. In that world, simply saying “trust the model” isn’t enough, but neither is making everything transparent if it compromises user privacy, proprietary methods, or safety.
That’s why the next phase of privacy tech will be defined by systems that can reconcile two demands that seem to pull in opposite directions: strong confidentiality and strong verification. Privacy without verifiability can undermine trust and accountability, while verifiability without privacy can expose users and institutions to unacceptable risk. The most durable infrastructure will be the kind that makes privacy practical, proofs accessible, and trust cryptographic – and that’s the vision ARPA has been building towards.
