At its core, it's a technology that enables data to remain encrypted and protected even while being actively used for calculations and analysis.
Confidential computing represents an important shift in how sensitive data can be processed in modern computing environments. At its core, it's a technology that enables data to remain encrypted and protected even while being actively used for calculations and analysis. Unlike traditional encryption methods that only protect data when it's stored or in transit, confidential computing maintains security throughout the entire data lifecycle, including during processing.
In the blockchain space, confidential computing is opening up new possibilities for privacy-preserving applications. Traditional public blockchains face a fundamental challenge: all transaction data and smart contract operations are visible to everyone, making them unsuitable for applications that need to handle sensitive information. Confidential computing helps bridge this gap by enabling "confidential smart contracts" - programs that can process private data while still maintaining the trust and transparency benefits of blockchain technology.
Several blockchain platforms have integrated confidential computing into their core architecture to enable private smart contract execution. This makes possible use cases that were previously impractical on public chains, such as private lending protocols that can evaluate creditworthiness without revealing personal financial information, or gaming applications that can keep certain game states hidden from other players.
Beyond gaming and finance, confidential computing in blockchain enables secure cross-chain communication, private oracle computations, and scalable off-chain processing. It allows sensitive computations to occur outside the main blockchain while maintaining security guarantees, effectively addressing both privacy and scalability challenges that have historically limited blockchain adoption.
The intersection of AI and confidential computing opens up new possibilities for privacy-preserving machine learning. One key application is protecting AI models themselves - preventing unauthorized access to proprietary algorithms while still allowing them to process data and return results. This is particularly important as AI models become valuable intellectual property.
The technology also enables "private inference," where users can interact with AI models without exposing their input data. For example, a medical diagnosis AI could analyze patient data within a TEE, providing results while keeping sensitive health information encrypted and secure. Similarly, AI trading agents can make decisions based on private market data without exposing their strategies or the data they're analyzing.
Another emerging use case is the creation of decentralized AI agents with private "thoughts." These agents can maintain encrypted internal states while interacting with public blockchain networks, enabling more sophisticated decision-making processes without compromising security. This approach could revolutionize how autonomous systems operate in decentralized environments.
In web3, zero-knowledge proofs have become a popular way to achieve privacy and confidentiality. However, TEEs and ZK proofs approach privacy in fundamentally different ways. While TEEs create secure hardware environments where private computations can occur, ZK proofs use pure mathematics to prove something is true without revealing the underlying information.
These differences lead to distinct trade-offs. TEEs generally perform better for complex computations and can handle a wider range of operations, making them more flexible for general-purpose applications. They can also process large amounts of data in real time. In contrast, ZK proofs often require specific circuits to be designed for each use case and can be computationally expensive to generate, though verification is typically fast.
Marko Stokic, Head of AI at Oasis Protocol Foundation
Marko Stokic is the Head of AI at the Oasis Protocol Foundation, where works with a team focused on developing cutting-edge AI applications integrated with blockchain technology. With a business background, Marko's interest in crypto was sparked by Bitcoin in 2017 and deepened through his experiences during the 2018 market crash. He pursued a master’s degree and gained expertise in venture capital, concentrating on enterprise AI startups before transitioning to a decentralized identity startup, where he developed privacy-preserving solutions. At Oasis, he merges strategic insight with technical knowledge to advocate for decentralized AI and confidential computing, educating the market on Oasis’ unique capabilities and fostering partnerships that empower developers. As an engaging public speaker, Marko shares insights on the future of AI, privacy, and security at industry events, positioning Oasis as a leader in responsible AI innovation.
About Oasis
Oasis Network is a Layer 1 blockchain platform focused on privacy, scalability, and versatility. It offers the first production-ready confidential EVM (Ethereum Virtual Machine) called Sapphire, enabling privacy-preserving smart contracts and decentralized applications. The network is expanding its focus to include AI applications, positioning itself at the intersection of blockchain, privacy, and artificial intelligence.
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