Bankless: The Wave of Privacy Technologies in Crypto Assets

Author: David C, Source: Bankless, Translated by: Shaw Golden Finance

As concerns about surveillance and data development grow, the cryptocurrency sector has recently accelerated its efforts to integrate Privacy-Enhancing Technologies (PET) into its core infrastructure.

Blockchain is designed to be completely transparent. While the crypto industry has long emphasized privacy methods (such as token mixers or privacy-based tokens), it has also been working to expand the scope of privacy (beyond simple DeFi and payments) without limiting privacy to dedicated networks.

As blockchain is increasingly applied in artificial intelligence training and institutional financing, the adoption of alternative cryptographic technologies is also becoming more popular. Four technologies are particularly hot: Multi-Party Computation ( MPC ), Fully Homomorphic Encryption ( FHE ), Trusted Execution Environment ( TEE ), and Zero-Knowledge Transmission Security Layer ( zkTLS ).

This article aims to showcase the role, use cases, and key projects based on each technology in enhancing privacy.

Multi-Party Computation (MPC)

MPC is a form of distributed computing that allows multiple parties to compute certain functions collaboratively without disclosing their own information.

Suppose you and five friends want to calculate your average salary, but do not want to disclose the specific amounts. Each person randomly divides their salary into six parts, with each person receiving one part. Everyone holds one part, but no one can reconstruct the salaries of others, as they only have one of the six parts required for the salary. Each person calculates using these six parts rather than the original salary. These results are combined to calculate the final average salary, without anyone knowing the specific salaries.

When regulatory restrictions or competitive concerns hinder direct data sharing, but collective analysis can benefit all parties, MPC becomes particularly important. A typical example is that multiple hospitals wish to use patient data to train AI—legal regulations prohibit the sharing of sensitive medical data, but MPC can enable collective training without actually sharing the data.

MPC's obstacles

As more and more people join the multiparty computing network, the management difficulty also increases. The system needs to transmit more messages among participants, and internet capacity limitations can lead to slower speeds. Everyone needs to perform more calculations, consuming more computing power. Although blockchain can prevent cheating by penalizing malicious actors who may collude in the network, it does not solve these resource and computing power issues.

Who is using MPC? For what purpose?

• Fireblocks - A custodial institution that uses MPC to split private keys between devices, ensuring that the complete key is never exposed.
• Arcium — A chain-agnostic network for private AI processing and sensitive tasks using MPC.
• Renegade——An on-chain dark pool for confidential transactions using MPC.

Fully Homomorphic Encryption (FHE)

FHE allows for data processing without decryption, which means that sensitive data remains encrypted during storage, transmission, and analysis.

Currently, data is encrypted during transmission, but it must be decrypted for processing, leading to a vulnerability window. For example, when I send a photo to the cloud, it is encrypted during transmission, but it is decrypted upon arrival. FHE eliminates this decryption step—data remains encrypted throughout the entire computation process, thereby protecting information during active use.

Imagine FHE as a locked safe with programmable gloves. You put private data and program instructions inside: "Add these numbers together," "Sort this list." You give the safe and gloves to someone else. They will blindly operate on the contents of the safe according to the instructions, without seeing what is inside. Once done, they will return the safe to you, and you can open it to get the correct result.

Obstacles of FHE

FHE will bring serious performance losses—computational speed will decrease by 10 to 100 times. Adding zero-knowledge proof (zkFHE) will further reduce the speed by several orders of magnitude. Developers want this combination because FHE can protect the input, but it does not guarantee the correctness of the operations. In other words, the issue is whether the person authorized to perform computations on data protected by FHE is actually executing the operations correctly. While the lack of this verifiability is an issue, adding it would make an already slow system almost unusable for real-time applications.

Who is using FHE? For what purpose?

• Zama —— FHE tool provider, implements encrypted smart contracts on EVM networks using tools like fhEVM.
• Fhenix - A research company that brings FHE into practical applications.
• PrivaSea - An AI training network for encrypted machine learning using Zama's FHE tools.
• Octra - A general-purpose chain using proprietary FHE for high-speed encrypted computing, featuring machine learning consensus and rentable services.

Trusted Execution Environment (TEE)

TEE is a secure hardware area that can isolate the storage and processing of data, preventing the rest of the machine (including the operating system and operators) from accessing that data.

If you have an iPhone, you interact with TEE every day, as Apple uses them to store biometric data. Here's how it works: TEE stores facial or fingerprint scan data in a secure chip area. When an application requests authentication, new scan data is sent to TEE for comparison. This comparison process takes place inside a sealed hardware environment—applications or the operating system cannot see any biometric data. TEE only returns "yes" or "no."

TEE has begun to appear in the cryptocurrency space for confidential smart contracts and computation. Uniswap's Layer-2 Unichain uses TEE to fairly build blocks and prevent MEV attacks.

obstacles of TEE

The integrity of TEE relies on hardware vendors rather than a distributed network, which makes them centralized under encryption standards. One might compromise TEE or exploit its vulnerabilities in a production environment. The Secret Network has encountered such a situation where researchers discovered a vulnerability in Intel chips that led to the decryption of all network transactions.

Who is using TEE? For what purpose?

• Space Computer —— A blockchain that uses TEE on satellite nodes, ensuring hardware tamper-proofing by operating in orbit.
• Oasis Protocol - Layer 1 uses TEE to implement confidential smart contracts with EVM-compatible runtime.
• Phala Network - A decentralized cloud platform for confidential computing using TEE from multiple hardware providers.

Zero-Knowledge Transfer Security Layer (zkTLS)

zkTLS combines TLS (which is used in HTTPS for internet security) with Zero-Knowledge Proofs (ZKP) to ensure the privacy and verifiability of information.

By adding zero-knowledge proof (ZKP), zkTLS allows users to transmit any HTTPS data (which accounts for 95% of network traffic) while controlling the information that is leaked. This enables any Web2 platform data to operate as a public API, unrestricted by platform permissions, thereby connecting the entire network and bridging Web2 and Web3.

For example, suppose you want to use your bank balance for an on-chain loan. You can access your bank account through the zkTLS tool, which can analyze any displayed data due to the bank using HTTPS. The tool will generate a zero-knowledge proof of your balance (ZKP) to prove the funds without revealing the specific amount or transaction history. You submit this proof to DeFi lending platforms, which will verify your credit status without accessing private financial data.

zkTLS obstacles

zkTLS is only applicable to data that is already displayed by the website—it cannot force the website to reveal hidden information. It relies on the continuous use of the TLS protocol and requires the involvement of real-time oracles, which introduces latency and trust assumptions.

Who is using zkTLS? For what purpose?

• ZKP2P: A on/off ramp protocol using zkTLS for private transfer of funds on and off the chain.
• EarniFi - A lending platform using zkTLS that offers privacy-protected loans for employees with earned but unpaid wages.
• DaisyPay - An application for influencer collaboration and instant payments using zkTLS.

Overall, each PET serves different objectives and has its own trade-offs. Applications may combine multiple PETs based on data requirements. A decentralized AI platform might use MPC for initial coordination, FHE for computation, and TEE for key management.

zkTLS has many different implementation methods that leverage various PETs in their architecture. These tools, when combined, can greatly expand the design space of cryptocurrencies and unleash their potential as the next generation of web iterations. It is well known that cryptocurrencies still need to improve user experience, which is crucial for enhancing the usability and widespread adoption of these privacy services.