Organizations across the US, UK, and other regulated markets face increasing pressure to use sensitive data safely.
Privacy enhancing technologies (PETs) provide a way to protect data while enabling secure analytics, AI, and collaboration across teams or institutions.
PETs help organizations stay compliant with regulations such as GDPR in the UK and EU, and HIPAA or FedRAMP in the US, while reducing the risk of data breaches.
They also make it possible to analyse data that would otherwise remain siloed, supporting informed decision-making without compromising trust or security.
PETs are tools, techniques, and cryptographic methods designed to protect sensitive data while it is stored, processed, or shared.
Privacy-Enhancing Technologies (PETs) are a set of technologies that enable organizations to use, analyze, and share data while minimizing exposure of sensitive information. They allow computation on data without revealing the raw data itself, helping meet privacy, security, and regulatory requirements.
Traditionally, data must be centralized and exposed to be analyzed. PETs break this trade-off by enabling value extraction without data disclosure, even when multiple parties are involved.
By embedding privacy directly into data systems, PETs help organizations comply with regulations and reduce the risk of data breaches.
Privacy enhancing technologies (PETs) are essential for organizations that need to use data safely while maintaining security and compliance.
By enabling controlled collaboration and analysis across teams, institutions, or borders, PETs allow organizations to work with sensitive or siloed data without exposing it.
Here’s how they help:
For regulated sectors such as healthcare, finance, and government, these benefits are critical.
Privacy enhancing technologies work by keeping sensitive data protected while it is being processed or shared.
Instead of moving or exposing raw data, PETs allow computations and analysis to happen in a secure way.
Typical processes include:
Privacy enhancing technologies are used in situations where organizations need to analyse or share sensitive data without exposing it.
They are most commonly applied in regulated industries and multi-party environments where privacy, security, and governance requirements are high.
Common use cases include:
These use cases allow organizations to work with sensitive data in environments where direct data sharing is not possible or permitted.
Homomorphic encryption allows computations to be performed directly on encrypted data, so data never needs to be decrypted during processing.
This enables analytics and machine learning workloads without revealing underlying values at any point.
Use case: Running predictive models on patient records without exposing PHI (protected health information)
Benefit: Full encryption throughout the data lifecycle
Secure multiparty computation enables multiple organizations to jointly compute results from their combined data while ensuring each party’s input remains private and undisclosed to others.
Use case: Financial institutions collaborating to detect fraud without sharing raw data
Benefit: No single party sees the entire dataset
Differential privacy introduces mathematically calibrated noise into outputs, ensuring results remain accurate at an aggregate level while preventing identification of individuals within the dataset.
Use case: Government agencies releasing population statistics safely
Benefit: Protects anonymity in large-scale datasets
Federated learning allows machine learning models to be trained across decentralized datasets. Only model updates are shared, while raw data stays within its original environment.
Use case: Medical institutions building AI diagnostics without centralizing patient data
Benefit: Keeps data local and reduces exposure risk
Trusted execution environments – TEEs provide hardware-based isolation that ensures code and data remain protected during execution, even in shared or untrusted infrastructure.
Use case: Protecting code execution in untrusted cloud environments
Benefit: Prevents tampering and unauthorized access control
Zero-knowledge proofs allow one party to prove a statement is true without revealing the underlying data, enabling verification without disclosure.
Use case: Verifying identity without exposing personal credentials
Benefit: Enables privacy-preserving authentication and verification
| Technology | Definition |
| Homomorphic Encryption | Lets organizations run calculations on encrypted data without ever decrypting it, keeping sensitive information secure while still allowing analysis. |
| Multiparty Computation (SMPC) | Multiple parties can work together to compute results using their own data without sharing the raw data with each other. |
| Differential Privacy | Adds controlled “noise” to data outputs so individual information can’t be identified, while still allowing useful insights from aggregated data. |
| Federated Learning | A machine learning approach where models are trained across multiple devices or locations without moving raw data, keeping information local and private. |
| Secure Enclave / Trusted Execution Environment (TEE) | A protected, isolated area in a computer or processor where code and data can be processed safely, even in untrusted environments. |
| Zero-Knowledge Proofs (ZKPs) | A method that proves a statement is true without revealing the underlying data, allowing verification without sharing sensitive information. |
| Synthetic Data | Artificial datasets that mimic real data patterns without containing actual personal information, enabling safe testing, analysis, and model training. |
As digital technologies expand, individuals and organizations are generating and handling more personal data than ever before. While this opens up new opportunities, it also introduces significant privacy and security risks.
PETs play a critical role in mitigating these risks by enabling secure data processing, protecting sensitive information, and ensuring compliance with evolving privacy regulations.
Adoption of PETs continues to accelerate, with the global market projected to reach USD 28.4 billion by 2034.
Protect your personal data and control who sees it. PETs ensure your information stays safe, reducing the risk of identity theft or unwanted surveillance.
Use case: A secure payment platform tokenizes your credit card info, so your sensitive data is never stored in its original form.
Example: Signing up for an online service using pseudonymized data keeps your real identity safe, even if the platform is breached.
PETs like encryption, differential privacy, and secure multiparty computation prevent unauthorized access to sensitive data.
Use case: Your health records are encrypted before storage in a hospital database, keeping them unreadable even if the system is hacked.
Example: A hospital stores patient lab results using encryption, so even if the database is breached, attackers cannot access the actual patient information.
PETs let organizations demonstrate that they value privacy, building confidence among citizens, clients, and stakeholders.
Use case: A government agency uses differential privacy to release population statistics, ensuring no individual citizen can be identified.
Example: When publishing census data, the agency aggregates and adds noise to datasets so researchers can access insights without exposing personal details, reducing the risk of misuse or privacy breaches.
Implementing PETs doesn’t have to be complicated. Organizations can start small and scale over time while ensuring sensitive data remains protected.
Key steps:
Use case: A municipal government wants to analyze citizen mobility data to improve public transportation planning. They use federated learning so the city can generate insights without accessing individuals’ raw location data.
Example: During a pilot, only aggregated traffic patterns are shared with planners, while each device keeps detailed location data private. This approach provides actionable insights without compromising citizen privacy.
PETs are evolving fast. As more organizations rely on sensitive data, they will be essential for secure collaboration, AI, and analytics.
Key trends shaping the future:
The future of PETs is about combining security, compliance, and innovation. Organizations that adopt these technologies early will gain a competitive edge while keeping sensitive data safe.
Choosing the right privacy-enhancing technology depends on your organization’s goals, data, and compliance requirements.
Key considerations:
Privacy-enhancing technologies differ from standard privacy approaches in several key ways:
This comparison helps organizations see why PETs are increasingly necessary for secure, innovative, and compliant data usage.
Many organizations misunderstand what PETs can do, which can slow adoption or lead to underutilization. Common misconceptions include:
Understanding these myths helps organizations set realistic expectations and adopt PETs effectively.
Privacy-enhancing technologies are a foundation for responsible and ethical AI. They allow organizations to:
By embedding privacy into AI workflows, PETs make it easier to balance innovation with ethical responsibility.
At Duality, we make privacy-enhancing technologies simple to use.
Our privacy-first platform for secure data collaboration brings together tools like homomorphic encryption, federated learning, differential privacy, and secure enclaves so your team can work with sensitive data safely. You can train AI models, validate results, and collaborate across teams and borders without moving the data.
Your data stays private and protected, while your organization can turn insights into decisions safely and confidently.
