Privacy-enhancing technologies (PETs): The future of data protection

Enter privacy-enhancing technologies (PETs), a class of tools designed to do exactly that. From enabling banks to detect fraud without sharing customer data to helping researchers collaborate without risking patient confidentiality, PETs are quickly becoming a cornerstone of secure data collaboration in the digital age.

Privacy-enhancing technologies (PETs) are a set of technologies, tools, techniques, and practices designed to protect personal data at every stage, from storage and processing to transmission. 

While the specific technologies may differ, their shared goal is clear: to safeguard individual privacy, even when sensitive data is being used for analysis or collaboration.

Different technologies serve different purposes based on privacy needs, data types, and technical environments. Here are the most important types:

1. Multi-Party Computation (MPC)

Multi-Party Computation allows multiple parties to jointly compute a result over their combined data without revealing any individual data inputs. It’s ideal for scenarios where no single party can be trusted with the full dataset.

2. Zero-knowledge proofs (ZKPs)

ZKPs enable one party to prove to another that a statement is true without revealing why it’s true. Think of proving your age without showing your actual date of birth.

3. FHE / HE (Fully / Homomorphic Encryption)

FHE allows computation on encrypted data without ever decrypting it. This is a powerful but computationally heavy approach, best suited for highly sensitive use cases like genomic research or classified government workloads. Partial HE is already used in some analytics engines.

4. Federated Learning

A machine learning approach where data never leaves the local device. Instead, models are trained across decentralized data sources and then aggregated.

5. TEE (Trusted Execution Environment)

TEEs provide secure hardware-based zones in a processor where sensitive code and data can be run in isolation from the rest of the system. Often used in confidential computing, TEEs are critical for enabling secure machine learning, cloud operations, and cryptographic key handling.

6. HSM (Hardware Security Module)

HSMs are physical devices that manage encryption keys securely. They’re foundational to digital signing, secure storage, and compliance-heavy environments such as finance and telecommunications. HSMs support key lifecycles, auditing, and integration with other PETs.

Privacy-enhancing technologies are no longer experimental—they’re powering real-world solutions across a wide range of sectors:

Healthcare

Hospitals across different countries can collaborate on medical research or clinical trials using MPC, keeping patient records private and GDPR-compliant.

Finance

Banks use PETs for anti-money laundering and fraud detection by analyzing transactions without revealing customer data. It’s a breakthrough in both privacy and compliance.

Education

Universities are replacing physical student IDs with decentralized digital IDs that verify identity without overexposing personal details. Biometric checks are paired with PETs to prevent exam cheating—all while protecting privacy.

Public sector

Governments can study income gaps, tax behavior, or population health without accessing raw citizen data. Secure computation enables accurate results without invading privacy. 

The focus here is not abstract theory, but real-world application. You'll gain a clearer understanding of what each technology enables, where it fits in, and how it can be applied to strategic decisions in your organization.

Decentralized Identity

Decentralized identity (DID) allows individuals to control their digital identity without relying on a central authority. Credentials are issued by trusted parties and stored in user-controlled wallets. It’s foundational for secure onboarding, cross-border identity checks, and eIDAS 2.0 compliance.

• Industries: Government, Education, Financial Services, Healthcare

• Use cases: eIDAS 2.0 compliance, digital public services, student identity management, patient onboarding

Self-Sovereign Identity (SSI)

SSI builds on DID by giving users total control over which credentials to share, with whom, and under what conditions. It eliminates over-collection of data and enhances privacy in workflows like university admissions, health verification, and border control.

• Industries: Education, Healthcare, Border Control, Travel

• Use cases: University admissions, health verification, visa checks, international mobility

KYC and Secure Data Sharing

Know Your Customer (KYC) processes require validating sensitive documents and personal data. PETs make it possible to verify key facts (e.g. age, residency, or legal status) without transferring or exposing full records. This supports compliance with GDPR and AML regulations while minimizing risk.

• Industries: Financial Services, Insurance, Real Estate

• Use cases: AML compliance, customer onboarding, tenant verification

Fraud Detection

PETs enable real-time detection of fraud patterns across datasets without compromising privacy. By running encrypted computations jointly across financial institutions, energy providers, or telcos, systemic abuse or anomalies can be flagged without exposing proprietary information.

• Industries: Finance, Energy, Telecommunications, Insurance

• Use cases: Transaction monitoring, billing integrity, claims review, subsidy abuse detection

Feedback Loop

In privacy-safe systems, feedback loops allow the outcomes of previous actions (e.g. a flagged transaction or a verified credential) to improve future processing. MPC and blockchain enable these loops with accountability and learning, without leaking sensitive data.

• Industries: Finance, Digital Identity, Telecom

• Use cases: Dynamic fraud modeling, credential reliability scoring, adaptive access control

Adopting PETs isn’t just about checking regulatory boxes—it’s about creating a foundation for secure and scalable innovation. Here’s what you gain:

• Data collaboration without data leaks: Organizations can finally work together on data-driven projects without sharing raw data.

• Regulatory compliance: PETs help businesses meet GDPR, HIPAA, and other global data privacy laws by keeping sensitive information hidden.

• Increased trust: Users and partners are more likely to engage with systems that protect their data by design.

• Cybersecurity resilience: PETs decentralize data storage and processing, reducing the risk of single-point failures or breaches.

• Insight without exposure: Make informed decisions using sensitive data without ever exposing it.

As powerful as PETs are, they come with some considerations:

• Performance overhead: Some PETs are still computationally heavy and may require optimization for real-time use.

• Complex implementation: Deploying PETs often requires cryptographic expertise and robust technical infrastructure.

• Interoperability issues: Integrating PETs into legacy systems can be challenging without a solid strategy.

• Cultural shift: Many organizations must shift their mindset from “protect by restricting access” to “protect by design.”

Still, these challenges are far from insurmountable, and the rapid growth of PET innovation is making adoption easier every year.

At Partisia, we believe that privacy and innovation should never be a trade-off. As data becomes the fuel of modern progress, privacy-enhancing technologies (PETs) are the infrastructure that makes responsible data use possible.

For us, PETs like Multi-Party Computation aren't just technical solutions. They're representing a paradigm shift. They allow organizations to collaborate securely, extract value from sensitive data, and meet strict privacy regulations without exposing what should stay confidential.

We see PETs as the foundation for a new kind of digital ecosystem: one built on trust, transparency, and secure data collaboration.

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