Privacy Enhancing Technologies: An Overview

So far, we’ve covered the basics of FHE: its mathematical foundation, the concept of homomorphism, and how bootstrapping comes in. It’s easy to see why FHE is often called the holy grail of cryptography. Still, that doesn’t mean other Privacy-Enhancing Technologies (PETs) aren’t important.

In this post, we look at the most advanced and widely used PETs, their core benefits, limitations, and where they shine. We focus on Multi-Party Computation (MPC), Zero-Knowledge Proofs (ZKP), Trusted Execution Environments (TEE), and Differential Privacy (DP).
TL;DR? We’ve got you: Below you have a a quick comparison table.

Multi-Party Computation (MPC)

MPC is a cryptographic technique that lets multiple parties compute a shared result without revealing their individual data to one another. No single party ever sees the full dataset: each performs computations on their private input and shares encrypted outputs. The result is visible to all participants, but original inputs remain private throughout. MPC has some drawbacks, including high communication overhead and reliance on active participation from all parties.

MPC was first used in the Danish Sugar Beet Auction (2008), where the new market clearing price of sugar beets was done by a computer program implementing Secure MPC between the farmers and Danisco, one of the biggest sugar producers in Europe at the time. Since then, MPC has been used in financial systems, key management and more.

Zero-Knowledge Proofs (ZKP)

ZKPs use cryptographic algorithms to generate a proof that can be verified without revealing the underlying data. They let one party (the prover) convince another (the verifier) that they know a fact or possess certain information, without disclosing the actual information.
In short, it’s a way to prove something is true without revealing why.

ZKPs are ideal for identity verification but face scalability challenges with large datasets and rollouts, where they can encounter performance bottlenecks. First introduced in 1985, ZKPs began seeing widespread practical use when cryptocurrency systems like Zcash applied them to create shielded, private transactions.
Since then, they have been used in identity systems, private financial transactions, blockchain security, and other privacy-preserving technologies.

Trusted Execution Environments (TEE)

A Trusted Execution Environment (TEE) is an isolated, hardware-backed enclave within a processor that ensures the confidentiality and integrity of code and data during execution. It prevents access or tampering by the main operating system, hypervisor, or any unauthorized application, even if those layers are compromised.

It’s like a “safe room” inside a processor where only authorized code can enter and nobody else on the system can peek at the memory.

TEEs emerged in mobile devices in the early 2000s, with Texas Instruments’ M-Shield and Arm’s TrustZone providing secure on-chip environments. Today, technologies like Intel’s SGX and AMD’s SEV are widely used for secure computing. Along with other platforms, they support confidential infrastructure-as-a-service, with deployments in digital advertising, secure payments, IoT safety, digital rights enforcement, and more.

Although hardware-based, TEEs are not immune to vulnerabilities and have made headlines due to security flaws and hacking incidents.

Differential Privacy (DP)

Differential Privacy is a privacy-preserving technique that protects sensitive information by ensuring that results reflect population-level trends without revealing information about specific individuals. . It works by adding carefully calibrated noise to statistical outputs, so the presence or absence of any single individual’s data does not significantly affect the result. This allows useful patterns to be observed while minimizing the risk of identifying personal information.

The mathematical foundations of differential privacy were formalized in 2006, in the first formal privacy analysis of a data anonymization process was made in 2008 using commute data collected by the US Census Bureau. Since then, DP has been deployed by major tech companies including Apple in MacOS, Google via its RAPPOR Technology, and Facebook.

However, it is worth noting DP is not without its limitations: The added noise reduces accuracy, making it difficult to balance privacy and accuracy challenging, especially when multiple queries are involved. Each analysis performed using DP must be verified by a cryptographer or statistician to ensure it meets privacy guarantees, making the technology reliant on expert human oversight. Moreover, because data is not encrypted in all stages, it remains vulnerable to side-channel attacks.

Privacy Enhancing Technologies – TL;DR Table

	MPC	ZKP	TEE	DP	FHE
Based on	Cryptography	Cryptography	Hardware	Statistics	Cryptography
Short Description	Allows multiple parties to jointly compute a result without revealing their private inputs.	Proves that a statement is true without revealing the underlying data.	A secure area inside a processor that runs code and stores data in isolation.	Adds statistical noise to outputs to protect individual privacy in datasets.	Enables computation directly on encrypted data without ever decrypting it.
Analogy	Solving a puzzle together while keeping your piece hidden.	Showing you solved a Sudoku without revealing the solution.	A safe room in a building that only authorized code can access.	Blurring a photo just enough that individuals can’t be recognized.	Solving a math problem without knowing what the numbers are
Strengths	Strong privacy, no need for a trusted third party, works on encrypted data.	Strong data privacy, fast verification, ideal for trust-minimized environments.	Hardware-enforced protection, efficient runtime	Provable privacy guarantees, works well on large datasets, good for analytics.	Maximum data privacy, computation on encrypted data, strong security guarantees.
Weaknesses	High communication overhead, requires online parties participation, partial encryption.	Computationally expensive, limited to specific use cases, some need trusted setup.	Vulnerable to hardware attacks, limited memory, vendor trust required.	Reduced accuracy, hard to tune, privacy loss across multiple queries.	Computationally intensive, slow compared to other PETs.
Great For	Collaborative analytics, secure voting, private machine learning.	Identity proofs, blockchain privacy, private authentication.	Secure payments, DRM enforcement, IoT safety.	Sharing aggregate data, public statistics, large-scale analytics.	Secure cloud computation, privacy-preserving AI, encrypted search and analytics.

Privacy Enhancing Technologies – Comparison conclusion

By now, you’ve gained a basic understanding of the leading privacy-enhancing technologies (PETs), where they shine, and what their limitations are. It’s worth noting that computational and communication overhead often prevents even the strongest cryptographic PETs from achieving widespread adoption.

This challenge is exactly what motivates Chain Reaction’s 3PU™, a purpose-built processor designed to accelerate FHE by orders of magnitude compared to general-purpose CPUs.

Next, we’ll explore the FHE ecosystem and how emerging libraries and compilers are making FHE implementations more accessible and practical.