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Privacy Enhancing Technologies

Privacy Enhancing Technologies (PETs) are a set of tools, techniques, and cryptographic methods designed to protect sensitive data at all stages: at rest, in transit, and during computation. They allow organizations to derive value from data without compromising individual privacy or exposing proprietary information.

Rather than relying solely on traditional perimeter defenses, PETs enable secure data usage by building privacy directly into the architecture of digital systems. 

How PETs Work

Privacy-enhancing technologies apply cryptographic protocols, algorithmic techniques, or system-level designs that limit or eliminate access to raw data during its lifecycle. Rather than exposing sensitive data, PETs enable organizations to analyze, share, or store information in a way that protects user privacy. These technologies are typically built into secure workflows, allowing privacy-preserving operations by default.

Why Privacy-Enhancing Technologies Matter

As data sharing and artificial intelligence adoption accelerate, so do privacy risks around surveillance, data leakage, regulatory violations, and misuse of personal information. PETs offer a practical solution: enabling collaboration, data analysis, and AI development without compromising privacy protection.

They help meet the demands of:

  • Data privacy regulatory requirements: GDPR, HIPAA, CCPA, etc.
  • Secure collaboration: Across teams, institutions, or borders
  • IP protection: Keeping proprietary algorithms and datasets confidential
  • Data localization laws: Supporting cross-border insights without transferring raw data

Types of Privacy-Enhancing Technologies

Homomorphic Encryption (HE)

Fully homomorphic encryption allows computations to be performed directly on encrypted data, without ever decrypting it. This means sensitive data remains protected even during data processing.

  • Use case: Running predictive models on patient records without exposing PHI
  • Benefit: Full encryption throughout the data lifecycle

Secure Multiparty Computation (SMPC)

Secure multi-party computation enables multiple parties to jointly compute a function over their inputs while keeping those inputs private.

  • Use case: Financial institutions collaborating to detect fraud without sharing raw data
  • Benefit: No single party sees the entire dataset

Differential Privacy

Adds mathematical noise to datasets before analysis, making contact tracing back to individuals statistically impossible.

  • Use case: Government agencies releasing population data safely
  • Benefit: Protects anonymity in large-scale datasets

Federated Learning

Allows machine learning models to be trained across multiple devices or locations without moving the data.

  • Use case: Medical institutions building AI diagnostics without centralizing patient data
  • Benefit: Keeps data local, reduces attack surfaces

Secure Enclaves / Trusted Execution Environments (TEEs)

Hardware-based isolation environments that execute code in a secure area of the processor.

  • Use case: Protecting code execution in untrusted cloud environments
  • Benefit: Prevents tampering and unauthorized access control

Zero-Knowledge Proofs (ZKPs)

Let one party prove to another that a statement is true without revealing the underlying data.

  • Use case: Verifying identity without exposing personal credentials
  • Benefit: Powerful for authentication and blockchain applications
Name Definition
Homomorphic Encryption Data and/or models encrypted at rest, in transit, and in use (ensuring sensitive data never needs to be decrypted), but still enables analysis of that data.
Multiparty Computation Allows multiple parties to perform joint computations on individual inputs without revealing the underlying data between them.
Differential Privacy Data aggregation method that adds randomized “noise” to the data; data cannot be reverse engineered to understand the original inputs.
Federated Learning Statistical analysis or model training on decentralized data sets; a traveling algorithm where the model gets “smarter” with every analysis of the data.
Secure Enclave/Trusted Execution Environment A physically isolated execution environment, usually a secure area of a main processor, that guarantees code and data loaded inside to be protected.
Zero-Knowledge Proofs Cryptographic method by which one party can prove to another party that a given statement is true without conveying any additional information apart from the fact that the statement is indeed true.

 

Where PETs Are Used

PETs are being adopted across industries that handle sensitive, regulated, or large datasets, including:

  • Healthcare: Sharing patient data across institutions securely.
  • Finance: Fraud detection using encrypted datasets.
  • Marketing: Analyzing consumer behavior without compromising identity.
  • Government: Secure identity verification and census data protection.

Benefits of Using PETs

  • Data Collaboration Without Risk: Work with external partners or internal teams without exposing sensitive information.
  • Regulatory Compliance: Align with global data privacy regulations without halting innovation.
  • IP and Confidentiality Protection: Protect both the data and the algorithms that operate on it.
  • Cross-Border Insights: Enable global data initiatives without violating data sovereignty laws.

Duality’s Approach to PETs

At Duality Technologies, we take privacy-enhancing technologies from concept to enterprise-grade implementation. Our platform integrates multiple PETs, including homomorphic encryption, federated learning, diffreential privacy and secure enclaves, into a scalable environment that supports collaborative data science and AI.

Duality enables responsible and secure AI by allowing organizations to train models, validate outcomes, and collaborate on sensitive data without data movement. This approach unlocks AI-ready data while maintaining privacy, auditability, and compliance across teams, borders, and industries.