TL;DR

• Garbled circuits were first introduced in 1986 by Andrew Yao.
• The concept evolved from theory into practical tools for privacy in blockchain, AI, and finance.
• Major upgrades like Free-XOR and Half-Gates made garbled circuits fast and scalable.
• Toolkits like JustGarble and EMP made them accessible to developers.
• COTI V2 is now using garbled circuits for private, quantum-resistant blockchain solutions.

Garbled circuits might sound complex, but their evolution is one of the most significant chapters in cryptography. From early theory to real-world implementations like COTI V2, discover how they’re quietly redefining the future of privacy.

Privacy in the Age of Data

In today's digital world, everything from your location to your spending habits is being tracked and processed. But what if we could analyze data without ever exposing it? That’s what garbled circuits enable.

First introduced in 1986, this cryptographic concept has matured into a powerful privacy tool. It now plays a role in blockchain, AI, finance, and even post-quantum security. This article walks through the full evolution of garbled circuits and how each phase contributed to the privacy tools we rely on today.

The Birth of Garbled Circuits (1986)

It all began with Andrew Yao, a Turing Award-winning computer scientist. He introduced the "Millionaires' Problem" - how can two people compare wealth without revealing their actual income?

To solve it, he proposed garbled circuits, a method that encrypts a logic function so that only the result is revealed, not the inputs.

This concept laid the groundwork for Secure Multi-Party Computation (MPC), where multiple parties collaborate on a computation without exposing private data.

How Garbled Circuits Work – The Basics

Garbled circuits convert a function into a hidden “circuit” where inputs and outputs remain private. Imagine two friends want to know who earns more without sharing their salaries. Garbled circuits make that possible.

The process encrypts each part of the circuit, sends it to the other party, and uses a method called oblivious transfer to retrieve the necessary keys without learning any additional details.

Here’s a simple visual of how Yao’s protocol works.

Yao explained it like this:

—Andrew Yao

The power of this method? It can be applied to any function that’s computable.

Early Challenges and Theoretical Progress (1986–1999)

Despite Yao’s breakthrough, garbled circuits remained mostly academic for years. The reasons were simple:

1. Computers weren’t powerful enough.
2. Research was focused more on traditional encryption than computing on encrypted data.

Still, progress continued. Scholars like Beaver, Goldreich, and Rabin expanded the theory behind secure computation.

Fairplay: The First Practical Garbled Circuit System (2004)

The first real implementation came in 2004 with Fairplay, developed by Lindell, Malkhi, and Pinkas. It was written in Java and could perform basic Boolean functions privately between two parties. (Boolean functions involve logic gates like AND, OR, and NOT.)

By 2008, FairplayMP extended the system to support multi-party computations, proving that garbled circuits weren’t just theoretical anymore.

Making Garbled Circuits Faster and Leaner (2008–2014)

Fairplay worked, but it was slow. Researchers made big leaps to optimize performance.

Here are a few key upgrades:

1. Free-XOR (2008) ~ Allowed XOR operations without encryption, saving resources.
2. Garbled Row Reduction (GRR3) ~ Reduced the number of encrypted messages per logic gate.
3. Point-and-Permute ~ Used position-based hints to speed up circuit evaluation.

Together, these improvements made garbled circuits 100x more efficient.

The Breakthrough That Made Garbled Circuits Practical (2015)

In 2015, a major leap came from Zahur, Evans, and Rosulek with the Half-Gates technique. Previously, each AND gate required 4 encrypted messages. Half-Gates cut that to just 2 without sacrificing security.

What that meant:

• Smaller data
• Faster execution
• Real-world readiness

They also released the EMP toolkit (Efficient Multi-Party), a C++ library that helped developers build privacy tools without starting from scratch.

This turned garbled circuits from an academic curiosity into a practical cryptographic tool.

Developer Toolkits Make Garbled Circuits Practical (2013–2018)

Between 2013 and 2018, open-source tools made garbled circuits more accessible. Before this, using secure computation required deep cryptography skills. Now, regular developers could start building privacy-focused apps.

Notable toolkits include:

1. JustGarble - A fast, C-based library by Bellare and team.
2. Obliv-C - A C language extension for secure multi-party programming.
3. ObliVM and TASTY - Popular in research labs and university courses.

These tools made it easier to experiment, prototype, and deploy especially in areas like secure voting, data analysis, and decentralized apps.

From Research to Reality: First Real-World Applications (2015–2020)

Garbled circuits moved beyond labs and into live systems.

1. Genomic Privacy ~ Compare DNA data without exposing full genomes.
2. Secure Voting ~ Let users verify their vote was counted without revealing identity or choice.
3. Private Finance ~ Run financial analytics without accessing raw numbers.

Projects were backed by organizations like DARPA, Intel, and Microsoft Research.

The Rise of Privacy in Blockchain and AI (2020–2023)

As blockchain and AI took off, so did the demand for private computation. Garbled circuits found new use cases:

• Private Smart Contracts ~ Logic runs on-chain, but inputs stay off-chain.
• Private Machine Learning ~ Make predictions without exposing training data.
• DeFi Protocols ~ Secure lending, trading, and auctions without revealing user info.

COTI V2: Garbled Circuits in Blockchain 2025

Launched in 2025, COTI V2 is a modern blockchain that uses garbled circuits to protect user privacy.

Key features include:

• Confidential transactions - Sender, receiver, and amount stay hidden.
• 1000x faster than Fully Homomorphic Encryption (FHE), making it viable for everyday use.
• Lighter than ZKPs, helping COTI scale efficiently.
• Quantum-ready, built to withstand future threats.

As CEO Shahaf Bar-Geffen put it:

The Post-Quantum & Federated Future (2023–2025)

The next chapter in privacy tech is already underway:

1. Quantum-Safe Garbling
Designing circuits to resist attacks from future quantum computers.

2. Federated Learning with MPC
Letting AI models train together across devices without sharing private data.

3. Hybrid Privacy Stacks
Combining different privacy techniques for stronger, layered protection.

Quick Definitions:

• ZKP ~ Proves something without revealing the data.
• FHE ~ Lets you compute encrypted data (very slow).
• TEE ~ A secure part of a computer, but can be physically attacked.
• Post-Quantum Crypto ~ Algorithms built to survive the quantum future.

Conclusion

What started as an academic idea in 1986 is now helping protect privacy across blockchain, AI, and finance.
Garbled circuits allow computations without exposing the data behind them, making them a core technology as the world pushes for secure, privacy-first infrastructure.