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As global connectivity continues to grow through universal internet adoption, concerns about data privacy have reached an all-time high. Nearly 90% of internet users consider online privacy a major issue. Addressing it requires rethinking and rebuilding core aspects of how the internet functions, such as designing systems to be privacy-preserving from the very beginning. To tackle these concerns, companies are turning to blockchain and cryptographic technology to revolutionize data protection and security in the Web3 age. It represents the next chapter in privacy in which a new tech stack where privacy is built-in by default, and not treated as an afterthought.

Privacy Throughout Web1 to Web3

Privacy and data storage have evolved significantly from Web1 to Web3, with Web3 offering to empower individuals by giving them full control over their personal information. In this new era, users no longer have to depend on centralized servers or sacrifice their data for internet access, because privacy is built into the foundation.

Web1 marked the birth of the internet as a content consumption platform, where users could only read static websites. Communication was one-way, and everything from content control to data storage was centralized in the hands of website owners. Web2 brought forth interactive platforms and user-generated content, shifting the focus to participation. While users could now create and share data, the infrastructure remained controlled by the major tech corporations that hosted and owned the servers and data.

Web3 changes the game. By decentralizing control and integrating privacy-preserving technologies, it allows users to share and access information without relinquishing ownership or control. However, actually executing on this vision isn’t as simple as it sounds. Many building block technologies must be in place to achieve utmost Web3 privacy preservation.

Privacy Preserving Networks

Privacy-preserving blockchains represent a specialized area within the crypto industry that’s aimed at strengthening the confidentiality and anonymity of user data and transactions. Despite this focus on privacy, they continue to uphold fundamental blockchain principles like transparency and immutability.

Their enhanced privacy measures are crucial because most traditional blockchains, like Bitcoin and Ethereum, are inherently open, with all transaction details publicly viewable. Networks and Web3 projects specifically geared toward privacy preservation may leverage a range of technologies toward such a purpose.

Privacy Preservation Technologies

Zero-knowledge Proofs: Zero-knowledge proofs enable one party (the prover) to confirm to another (the verifier) that a specific statement is true, without disclosing any additional information. This tech can validate a transaction’s authenticity without exposing its details or the identities of the participants. ARPA Network rests at the forefront of research and development into zero-knowledge technologies, especially zk-SNARK use cases in verifiable AI agents.

Ring Signatures: Ring signatures allow a user to sign a transaction on behalf of a group, without revealing which individual in the group actually signed it. This adds anonymity by obscuring the sender’s identity.

Homomorphic Encryption: This encryption method enables computations on encrypted data without decrypting it. On the blockchain, it allows for secure transaction validation and processing without exposing sensitive information.

Private Transactions and Smart Contracts: These transactions use advanced cryptographic methods to hide the amounts being transferred, while still ensuring that the total inputs and outputs are balanced. This prevents external observers from seeing the exact figures involved. Smart contracts on privacy-focused blockchains can operate using encrypted or private data, enabling secure and confidential interactions beyond basic transfers.

Decentralized Identity (DID): DID frameworks give users control over their digital identities, allowing them to disclose only the necessary information while keeping the rest of their personal data private. They leverage verifiable credentials, which are cryptographically secured attestations, to bundle identity attributes and allow users to share only the specific information required for a given interaction.

Moving Forward

In essence, privacy-preserving blockchains achieve a balance between the openness and security that blockchain technology offers and the essential need to protect sensitive data and user privacy. By tackling these concerns, they expand blockchain’s practical applications and support its ongoing development and relevance. Powering this initiative is the development of zero-knowledge technology which ARPA Network has continuously contributed to. Privacy preserving networks are especially useful in areas such as financial services, supply chain tracking, and healthcare data management, allowing participants to engage securely while keeping confidential information protected.

About ARPA

ARPA Network (ARPA) is a decentralized secure computation network built to improve the fairness, security, and privacy of blockchains. ARPA threshold BLS signature network serves as the infrastructure of verifiable Random Number Generator (RNG), secure wallet, cross-chain bridge, and decentralized custody across multiple blockchains.

ARPA was previously known as ARPA Chain, a privacy-preserving Multi-party Computation (MPC) network founded in 2018. ARPA Mainnet has completed over 224,000 computation tasks in the past years. Our experience in MPC and other cryptography laid the foundation for our innovative threshold BLS signature schemes (TSS-BLS) system design and led us to today’s ARPA Network.

Randcast, a verifiable Random Number Generator (RNG), is the first application that leverages ARPA as infrastructure. Randcast offers a cryptographically generated random source with superior security and low cost compared to other solutions. Metaverse, game, lottery, NFT minting and whitelisting, key generation, and blockchain validator task distribution can benefit from Randcast’s tamper-proof randomness.

For more information about ARPA or to join our team, please contact us at contact@arpanetwork.io.

Learn about ARPA’s recent official news：

Twitter: @arpaofficial

Medium: https://medium.com/@arpa

Discord: https://dsc.gg/arpa-network

Telegram (English): https://t.me/arpa_community

Telegram (Turkish): https://t.me/Arpa_Turkey

Telegram (Korean): https://t.me/ARPA_Korea

Reddit: https://www.reddit.com/r/arpachain/

In today’s digital world, privacy has become both a right and a rare luxury. The modern internet was built on centralized data silos, and has transformed personal information into the most valuable commodity of the 21st century. Every online interaction, data point, and click contributes to an ever-growing digital footprint controlled not by individuals but by corporations. As we move toward a new era powered by artificial intelligence (AI) and blockchain, the question is no longer whether privacy should matter but about how to preserve it without sacrificing innovation. This is where privacy-preserving AI and Web3 technologies come into play.

The Privacy Paradigm Shift: Web2 to Web3

In the early days of Web2, the internet promised connection and convenience but it came at the cost of privacy. Platforms like social media giants and cloud providers gained centralized control as user data became a resource to be harvested, monetized, and analyzed. Individual users themselves have little oversight of how their information was used or shared. Web3 introduces a radically different model of decentralization. Instead of entrusting sensitive data to a single entity, Web3 leverages distributed networks, smart contracts, and cryptography to ensure that users own and control their digital identity. This shift represents an ethical upgrade as much as a technical one. Privacy-preserving tools such as zero-knowledge proofs (ZKPs) and multi-party computation (MPC) enable computations and validations without revealing private data. These cryptographic breakthroughs have opened the door to a new kind of internet where trust is algorithmic and not institutional.

The Rise of Privacy-Preserving & Verifiable AI

While Web3 solves the trust problem in data infrastructure, AI brings a new challenge of the need for verifiable intelligence. AI systems rely on massive datasets for training and inference, but those datasets often contain sensitive personal information.

This is where privacy-preserving, verifiable AI enters the picture. It aims to build intelligent systems that can learn, predict, and generate insights without compromising data confidentiality. Techniques like federated learning, ZKML (Zero-Knowledge Machine Learning), and secure enclaves make it possible for AI models to operate in a privacy-conscious manner. For instance, federated learning allows multiple entities to collaboratively train models without sharing raw data. Each participant contributes to improving the AI while their information remains locally secured. Combined with verifiable computation and blockchain-based auditing, privacy-preserving AI ensures that transparency and confidentiality can coexist. Without privacy mechanisms, AI systems risk becoming opaque black boxes, and Web3 applications risk replicating the same surveillance structures that Web2 was criticized for.

Privacy-preserving AI ensures:

Accountability: Computations can be verified without exposing private inputs.

Security: Sensitive data remains protected, even in multi-party environments.

Fairness: Decentralized applications can maintain integrity without bias or manipulation.

ARPA’s Role in Privacy-Preserving Computation

At the forefront of this movement is ARPA Network. Since our inception, we have been pioneering cryptographic solutions that make privacy practical and scalable in decentralized systems. ARPA’s flagship product Randcast brings verifiable randomness to Web3 ecosystems — powering gaming, AI, and agentic systems with trustless, tamper-proof random number generation. Beyond randomness, ARPA’s work in multi-party computation (MPC) and verifiable computation underpins a new layer of privacy infrastructure for decentralized applications. Our recent research into ZK-SNARKs represents another step toward building verifiable AI systems with fundamental use cases for Web3. In the world of AI agents, privacy is even more crucial. As AI agents start performing financial transactions, processing user data, and interacting autonomously on behalf of their owners, their underlying computations must remain verifiable yet private. ARPA’s cryptographic infrastructure ensures these agents operate transparently without leaking sensitive information.

With the new internet being increasingly driven by AI agents, data markets, and autonomous systems, ARPA provides the necessary privacy backbone to ensure that computations are verifiable and private while still being fast and fair.

Looking Ahead to 2026 for Privacy-Preserving, Verifiable AI

As we move into 2026, the momentum behind privacy-preserving and verifiable AI is poised to accelerate even further. Several converging trends suggest that the next year could mark a turning point not only in adoption, but in how developers and users conceive of trust, privacy, and intelligence in digital systems.

Widespread Adoption of Privacy by Default. Increasing public scrutiny of data leaks, privacy breaches, and misuse of personal information is fueling demand for technologies that offer strong privacy guarantees out of the box. In 2026, we expect protocols built on techniques like zero-knowledge proofs (ZK-proofs), secure multi-party computation (MPC), and federated learning to become baseline expectations.

AI Agents Entering Real-World, High-Stakes Use Cases. As AI agents evolve from simple chatbots to autonomous systems, the need for verifiable trust becomes non-negotiable. In 2026 we expect to see verifiable AI frameworks increasingly used in real-world high-stakes tasks such as DeFi, governance, autonomous DAOs, AI-driven lending, or on-chain identity systems.

Privacy + Utility Convergence Opens New Markets. Historically, there was often a trade-off: you got privacy or utility. But with advances in cryptographic techniques and decentralized infrastructure, 2026 may well be the year when that compromise ends. This convergence could unlock new markets like private health analytics, private identity verification, decentralized data marketplaces, and more.

Greater Interoperability Across Chains and Applications. As more projects adopt privacy-preserving AI, interoperability and standardization will matter. In 2026, cross-chain and cross-application privacy standards that enable AI models and verifiable computations to move between networks will become more important.

User Empowerment and Ownership. Ultimately, privacy-preserving AI empowers users to reclaim control over their data. In 2026, we expect growth in user-owned AI agents, personal data vaults, and “privacy-first” applications where users can audit AI behavior themselves.

Taken together, these trends suggest 2026 may mark a turning point that can catalyze the mainstream adoption of privacy-preserving, verifiable AI. The groundwork laid by protocols like ARPA, and by early adopters integrating trusted computation, may begin to pay off in real value: safer AI agents, transparent intelligence, and renewed faith in decentralized systems.

About ARPA

ARPA Network (ARPA) is a decentralized, secure computation network built to improve the fairness, security, and privacy of blockchains. The ARPA threshold BLS signature network serves as the infrastructure for a verifiable Random Number Generator (RNG), secure wallet, cross-chain bridge, and decentralized custody across multiple blockchains.

ARPA was previously known as ARPA Chain, a privacy-preserving Multi-party Computation (MPC) network founded in 2018. ARPA Mainnet has completed over 224,000 computation tasks in the past years. Our experience in MPC and other cryptography laid the foundation for our innovative threshold BLS signature schemes (TSS-BLS) system design and led us to today’s ARPA Network.

Randcast, a verifiable Random Number Generator (RNG), is the first application that leverages ARPA as infrastructure. Randcast offers a cryptographically generated random source with superior security and low cost compared to other solutions. Metaverse, game, lottery, NFT minting and whitelisting, key generation, and blockchain validator task distribution can benefit from Randcast’s tamper-proof randomness.

For more information about ARPA, please contact us at contact@arpanetwork.io.

Learn about ARPA’s recent official news:

Twitter: @arpaofficial

Medium: https://medium.com/@arpa

Discord: https://dsc.gg/arpa-network

Telegram (English): https://t.me/arpa_community

Telegram (Turkish): https://t.me/Arpa_Turkey

Telegram (Korean): https://t.me/ARPA_Korea

Reddit: https://www.reddit.com/r/arpachain/