What are Privacy-Enhancing Technologies (PETs)? Types & Selection Guide
Strict privacy laws limit your ability to share and use data for research, testing, and development. This is why privacy-enhancing technologies are critical for any business, small and large, as they’re designed to help comply with privacy and data protection regulations.
But the fact is, not everyone understands what privacy-enhancing technologies are and how they differ from other security tools. If that includes you — you’ve come to the right place.
This article will describe different privacy-enhancing technologies, examples of use cases for businesses, and their potential advantages. We will also help you choose the right type of PET for your organization.
Table of Contents
Privacy-Enhancing Technologies Definition
PET (privacy-enhancing technology) encompasses tools that help protect personally identifiable information (PII) and minimize security risks. The examples of privacy-enhancing technologies include software, algorithms, methodologies, and physical components (like hardware keys).
PETs are essential for businesses that must responsibly handle sensitive customer and corporate data, as well as comply with data privacy regulations without compromising functionality. They can safeguard information for testing, development, research, or service-improvement.
While compliance is the primary goal of PETs, companies implement them for different reasons.
What is the Role of Privacy-Enhancing Technologies for Businesses?
Businesses deal with datasets that contain a lot of sensitive information. Adopting PETs can prevent many privacy-related problems and unlock several business advantages.
- Compliance with privacy and data protection laws: PETs help minimize the collection of sensitive data, facilitate consent acquisition for data sharing, and minimize PII in business systems and databases. This allows you to comply with stringent data protection regulations, like the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and the Health Insurance Portability and Accountability Act (HIPAA).
- Reduced damage from data breaches: By using technologies such as encryption, data masking, and pseudonymization, companies reduce the amount of sensitive data in their possession. The processed data is rendered useless without keys or becomes anonymized. Thus, businesses avoid reputational damage from leaks, potential civil cases, and non-compliance fines from regulators.
- Secure data sharing and collaboration: Privacy-enhancing technology allows companies to safely share data between employees and third-party organizations without regulatory oversight. Such secure sharing is necessary for businesses that rely on external partners for data analysis and improves collaboration projects.
- Data misuse prevention: What privacy-enhancing technologies do is strip datasets of sensitive information. In other words, companies reduce the risk of their employees unintentionally misusing data due to poor knowledge of internal policies and software bugs.
- Automated consent approval: Privacy-enhancing technology can streamline the processes of notifying users about what happens with their records and PII. It also makes it easier to receive approval from customers to use their data.
Not all PETs keep your data equally protected or ensured. It depends on the type of tool and technique employed.
Examples of Privacy-Enhancing Technologies (PETs)
PETs include various techniques for privacy and data protection. Let’s describe the most common methods that are accessible for businesses today.
Homomorphic encryption
Homomorphic encryption allows computations on encrypted data without needing to decrypt it. When decrypted, encrypted data will match the outcome of operations on the original data. This cryptographic technique maintains data privacy for data processing, analysis, and sharing.
The main issue with this technique is its computational complexity, as operations on encrypted data are slower than on plaintext. Implementing homomorphic encryption also requires advanced cryptographic expertise.
Use cases for businesses:
- Analyzing the transactions safely for financial risk assessments and fraud detection on transaction data.
- Outsourcing data to cloud-based data processing environments for research without exposing sensitive information.
Secure Multi-Party Computation (SMPC)
SMPC (or just MCP) allows multiple parties to compute a function using inputs and view a public output while preserving data confidentiality. Companies, researchers, and users can aggregate and analyze values from multiple data sources without privacy compromises.
Like homomorphic encryption, secure multi-party computation introduces computational overhead and requires significant processing power. Besides, for SMPC to work, multiple parties should have a mutual trust network and compatible infrastructure.
Use cases for businesses:
- Collaborating on medical research that involves sensitive patient data from different healthcare providers.
- Securely sharing and analyzing data among manufacturing partners to optimize operations.
Differential Privacy
Differential privacy is a mathematical framework that introduces controlled randomness (noise) into datasets to the real PII. The primary advantage of differential privacy is that you can measure the level of privacy. You can add the exact amount of noise to maintain the data utility. Still, companies need expertise to avoid producing inaccurate or misleading data.
Use cases for businesses:
- Collecting accurate data to improve online services while preserving individual privacy.
- Facilitating research and innovation in public research and policy.
Zero-Knowledge Proofs (ZKP)
ZKP is a cryptographic verification method that allows one party to prove that they possess knowledge about data without revealing its contents. The verifier cannot access or modify the original input — it can only understand if the statement is valid.
This method is primarily used for transaction verifications. It also requires intense processing power to generate and verify proofs.
Use cases for businesses:
- Authenticating data for online services without exposing personal information.
- Verifying transactions securely in blockchain networks and Decentralized Autonomous Organizations (DAOs).
Data Masking Techniques
Data masking techniques include removing, altering, or obfuscating data to safeguard sensitive information. These techniques allow organizations to use realistic data for testing, development, and research. Examples of privacy-enhancing technologies of data masking include:
| Description | Example | |
|---|---|---|
| Pseudonymization | Replaces personal identifiers with pseudonyms. The process can be reversed and re-identified with auxiliary data. | Replacing "Michael Smith" with "User12345" in a customer database. |
| Generalization (Aggregation) | Anonymizes data by grouping similar data together, making it less detailed and harder to identify. | Age information "32" becomes "30-39". |
| Randomization | Alters values with random and mock data to preserve confidentiality. | Changing a phone number from "+1-212-456-7890" to "+1-212-765-9834". |
| Data Minimization | Limits data collection to only what is necessary for specific purposes. | Masking all but the last three digits of a credit card number (**** **** **** *123). |
| Perturbation | Modifies original datasets by adding random noise or rounding values. | Adding random noise to salary data: "$50,000" becomes "$50,257". |
| Swapping | Rearranges attribute values in a dataset to mask original data. | Swapping birthdates between records ("1990-05-12" in one record and "1985-08-23" in another). |
However, most of these techniques come with compromises. Usually, you have to combine masking with other privacy-enhancing technologies to prevent the risk of re-identification.
Use cases for businesses:
- Training machine learning models with anonymized patient data to improve diagnostic tools.
- Protecting sensitive customer information during compliance audits in the finance sector.
Federated learning
Federated learning is a decentralized machine learning technique in which models are trained across multiple locations. Each device trains the model locally and only shares the updated parameters, which are used to upgrade the model.
While very secure, federated learning is very complex when coordinating across devices. If not properly secured, the aggregated model parameters are also vulnerable to attacks.
Use cases for businesses:
- Developing predictive models across multiple hospitals without sharing patient records.
- Improving fraud detection algorithms across different banks while maintaining data privacy.
Trusted execution environment (TEE)
TEE (or Secure Enclave) is a physically isolated location, usually within a primary processor, that safeguards code and data from operating systems and other applications. It’s a hardware-based PET where you can store and execute code without the risks of unauthorized access and malware.
However, the security and scalability of the TEE largely depend on the capabilities of your processor hardware. If malicious actors discover hardware vulnerabilities, you risk compromising the entire environment.
Use cases for businesses:
- Ensuring secure computations of code for software testing and development.
- Securing data processing for financial institutions in a shared public cloud.
AI-generated synthetic data
Synthetic data is fully generated and compliant information that mimics real-world data. AI-generated datasets don’t contain personal data or indirect identifiers, exempting them from data privacy laws. In other words, you are free to use and share this data without regulatory oversights.
Synthetic data generation is one of the most confidential privacy-enhancing technologies. Unlike most anonymization and data masking methods, synthetic data platforms preserve the structural relationships in data, making it suitable for advanced research and development.
Synthetic data requires sophisticated algorithms to generate data that accurately represents the actual data. That’s why companies should only choose a reputable synthetic platform.
Use cases for businesses:
- Enhancing AI models for mobile devices to improve user experience without risking sensitive data.
- Enabling privacy-preserving sharing of synthetic datasets for collaborative research and innovation.
- Creating diverse training scenarios for advanced medical research that requires accurate and diverse data.
- Monetizing data by selling compliant and highly accurate datasets to other companies.
Companies usually employ several PETs to ensure efficient data governance and data confidentiality. But, in addition to technologies, companies should commit to efficient practices.
Strategies to Improve PET Efficiency for User Privacy and Data Governance
A few organizational-level strategies are necessary to make PETs more efficient and ensure data confidentiality.
- Standardize data formats: Maintain consistent data formats and protocols to easily integrate different PETs and facilitate data exchange across systems.
- Establish data processing standards: Develop and enforce clear codes of conduct and standards for collecting, storing, processing, and sharing data.
- Integrate into cybersecurity: PETs like homomorphic encryption, data masking, and TEE mitigate risks from unamortized access, effectively maximizing data security.
- Educate your employees: By ensuring that your team is well-informed about the drawbacks of different PETs, you can gradually improve the data protection in your organization.
- Minimize collected data: Less data means fewer risks. Limit the amount of personal data you collect for research and service-related purposes, regularly review your data governance practices, and delete unnecessary datasets.
- Update your PETs to reduce risks: Stay informed about technological advancements and incorporate new practices to remain secure against emerging threats.
Following these practices will make implementation of data privacy-enhancing technologies easier in the long run.
How to Choose the Right Privacy-Enhancing Tool for Your Business
Incorporating these practices into your data governance strategy will help you leverage PETs to enhance privacy, security, and data utility.
1. Identify specific use cases and requirements
Begin by auditing all personal data your organization collects and classifying it based on sensitivity. Create a prioritization matrix based on the data’s sensitivity, ranking types of data based on risk level. Most companies want to incorporate PETS gradually, so this will help you focus on the technologies that add the most value first.
Then, outline the common data use cases into specific scenarios. For example, if you aim to secure customer data, identify the types of customer interactions that require protection (like transactions, ad targeting, or customer support conversations).
2. Assess compatibility with existing systems
Analyze your current IT infrastructure, communication protocols, and data formats. This will help you understand the compatibility requirements for your new PETs. Prioritize the technologies that require minimal changes to your setup. It also helps to try platforms with a free trial to see how well they integrate with your toolset.
3. Evaluate PETs based on privacy protection levels
Compare PET providers based on your needs for privacy, usability, and performance. For example, your team can manually analyze the accuracy of anonymized datasets against the real data or try to re-identify the data back to the original form.
Assess the long-term viability of the PETs, considering the maintenance requirements, potential technical debt, and scalability. Make sure you can migrate the data and workflow to other platforms.
4. Compress the costs and payment models
Evaluate the total cost of ownership for each PET. This requires you to compare the direct subscription or license costs, as well as expenses for integration, training, or the adoption period.
If you need to get the higher management onboard, calculate the expected return on investment for each PET. The costs are qualitative (like enhanced customer trust and less regulatory oversight) and quantitative (like fewer losses from data breaches).
5. Review vendor reputation and feedback
Look for vendors with customer reviews and testimonials. Focus on what other companies say about the service support level, implementation challenges, and ease of training. Look for specific examples of how vendors have addressed challenges similar to yours. A reliable partner will help you set up the PET by providing integration assistance, training, and comprehensive support.
Synthetic Data as a Privacy-Enhancing Technology
Privacy-enhancing technologies (PETs) are downright critical for privacy and data protection. However, not all PETs can make your data fully compliant or maintain data usability for advanced research. You should combine several techniques and practices to ensure data confidentiality and security.
Synthetic data generation is an excellent way to get high-quality, privacy-first data that mirrors real data. Syntho’s smart synthetic data generation platform allows you to create such data on a whim for various use cases, from healthcare research to algorithm training.
About the author
Marijn Vonk
Chief Product Officer & Co-founder
Marijn has an academic background in computing science, industrial engineering, and finance, and has since then excelled in roles across software product development, data analytics, and cyber security. Marijn is now acting as founder and Chief Product Officer (CPO) at Syntho, driving innovation and strategic vision at the forefront of technology.
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