Privacy-enhancing technologies (PETs) are tools and methods that protect personal data throughout its lifecycle, and are pivotal for the deployment of private AI. They help ensure privacy by enabling the analysis or sharing of information while shielding individual identities or sensitive details, including when processing data with Large Language Models (LLMs) and other advanced systems.

These methods belong to the wider field of data protection, which focuses on securing personal information against threats such as misuse, leaks, or unauthorized access. Traditional data protection relies on restricting access or obtaining user consent, but PETs take it further by embedding privacy directly into data system designs. They allow organizations to handle data responsibly without exposing it unnecessarily.

The technology is developed by applying cryptographic techniques and privacy-focused system designs to the ways data is stored, processed, or shared. Companies in sectors such as healthcare and finance can run analytics across sensitive datasets, such as patient records or transaction logs, without revealing personal details. 

Key types of privacy-enhancing technologies 

There are several categories of privacy-enhancing technologies, which are all designed to keep personal data protected during use cases such as analysis or storage.

Below are the key approaches used in enterprise settings:

Cryptographic methods

  • Homomorphic encryption: A method that allows data to be encrypted and still used in calculations, so results can be produced without exposing raw data. It is of particular use in finance, where encrypted client records can be analyzed without revealing identities, and can be integrated with Retrieval-Augmented Generation (RAG) systems for secure information retrieval.
  • Secure multiparty computation (SMPC): This approach enables multiple parties to work together on a shared task without revealing individual data to one another. For example, several hospitals can compare treatment outcomes without sharing patient records.
  • Zero-knowledge proof: A technique that proves a statement is true without showing the underlying data. In identity verification, it allows a user to prove they meet age or residency requirements without disclosing full personal details.

Data anonymization & synthesis

  • Synthetic data: The process involves artificially generating data based on patterns in real datasets. While it mimics the original data structure, it contains no actual user information, making it ideal for testing or model training.
  • Differential privacy: This technique introduces controlled noise into datasets to protect individual entries. Technology companies use it to gather insights from user behavior while keeping individual activity hidden.

Privacy-preserving computation

  • Federated learning: A strategy where machine learning models are trained across multiple devices or servers without moving the data. It is widely used in healthcare applications.
  • Confidential computing: An approach that protects data in use by isolating it during processing, often implemented as part of a comprehensive privacy orchestration layer that manages secure data flows across systems.
  • Trusted execution environments (TEEs): These are secure areas within a processor that keep data and code isolated when a task is running. They are often used to safeguard sensitive operations in financial systems.

Benefits of using privacy-enhancing technologies 

As businesses adopt new technologies, more systems are accessing personal information. Organizations face growing pressure to comply with evolving data protection requirements.

Research suggests PETs will be central to that effort, with the global market projected to reach $28.4 billion by 2034.

Below are key ways that PETs deliver practical benefits in enterprise settings.

Strengthen data protection and reduce the risk of breaches

Companies using PETs limit their exposure to raw data and help to prevent unauthorized access when they are processing or sharing it.

IBM reveals the global average cost of a data breach in 2024 was $4.88 million, reinforcing the need for stronger privacy controls.

Help meet privacy regulations (e.g., GDPR, HIPAA)

Companies need to demonstrate that personal information is protected by design to comply with regulations such as HIPAA in the US and GDPR in Europe. Using PETs ensures exposure to identifiable data is minimized throughout its lifecycle, supporting enterprise compliance goals.

Allow secure sharing and joint use of sensitive data

PETs make it possible for organizations to work together on shared data projects without exchanging raw data. For example, banks can use secure multiparty computation to detect fraud patterns across institutions without revealing individual customer data.

Build consumer trust and support responsible innovation

When privacy is embedded into products and services, customers are more likely to trust how their data is handled. According to Cisco, 95% of security professionals say customers are unlikely to buy if data is not properly protected — highlighting the business risk of poor privacy practices.

Privacy-enhancing technologies use cases 

PETs are being applied across industries to solve practical enterprise challenges. They unlock new opportunities for innovation and collaboration, as they allow organizations to work with sensitive data without compromising privacy. 

Below are enterprise use cases that demonstrate how PETs support data-driven goals while preserving individual privacy.

Healthcare data analysis and sharing

Hospitals and research institutions use PETs to study patient outcomes without sharing raw medical records. Federated learning allows each organization to share trained models locally, so patient information remains within its original system. Researchers can improve diagnostic models and compare treatment effectiveness while ensuring strict data privacy is maintained.

Financial fraud detection and compliance

Banks apply secure multiparty computation to detect fraud across datasets held by separate institutions. During the process, no customer information is shared among institutions, which reduces the risk of exposure. PETs also support compliance by keeping sensitive financial data protected during audits and internal reporting.

Retail customer behavior analysis

Retailers use differential privacy to explore customer trends without exposing personal information. Analysts can investigate how shoppers respond to promotions or navigate physical store layouts while maintaining their anonymity through advanced Natural Language Processing (NLP) techniques with built-in privacy safeguards. Teams can improve marketing strategies and optimize store design without compromising individual privacy.

FAQs