What is Privacy Enhancing Technology (PETs)?

With the rise of a digital world economy, huge amounts of personal data are collected and analyzed every second of the day. Through the use of big data, great progress can be achieved in such fields as personalized medicine and retail. 

Nevertheless, any data sharing and analysis pose significant problems related to exposure to risks of violations and breaches of confidentiality.  Knowing how privacy enhancing technology works can help address this problem effectively. 

The use of computational techniques allows companies to gain important insights and train their AI without any disclosure of personal information.

The Fundamentals: What is Privacy Enhancing Technology?

In its simplest sense, privacy-enhancing technology (PETs for short) is a combination of hardware, software, and mathematics that ensures data confidentiality at all stages of its lifecycle. 

While basic security measures merely serve to secure the perimeter of the network, PETs change, hide, or isolate data through mathematics. Such an architecture makes it possible to use data safely to process, analyze, and share it. 

Integrating privacy requirements into the technical processes allows companies to cooperate with outside organizations without violating any privacy requirements.

Types of Privacy Enhancing Technology

Today’s companies apply various PET solutions based on their particular technical environment and collaborative purposes:

• Synthetic Data Generation

  In this method, AI technologies generate entirely new data sets that have the same statistical properties and dependencies as the real-life data set. Programmers are able to test their software and machine learning models without access to real customer data.

• Differential Privacy

  In this approach, noise is added to the queries to the data sets. This technology allows for the identification of general trends in large populations but makes it mathematically impossible to identify individuals.

• Homomorphic Encryption

  A more complex form of encryption, in which math operations can be done to the encrypted data without the need for decrypting it first. This allows for safe processing of sensitive data by third parties even if the data is fully unreadable.

• Trusted Execution Environment (TEE)

  Secure enclaves within computer processors that isolate the execution of code/data from the main host operating system.

Advantages of Applying PETs in Businesses

Incorporating advanced privacy technology provides many benefits for expanding businesses other than traditional risk management:

• High Value Data Collaboration

  It is often required to collaborate on data outside departments or even between companies. In such cases, the use of methods such as federated learning or multi-party computation allows analyzing data collaboratively without sharing raw proprietary customer data.

• Simplification of Regulatory Compliance

  Global privacy frameworks like GDPR, HIPAA, and CCPA impose strong data minimization principles and privacy protection measures. The application of PETs allows compliance with “privacy by design” principles, thus minimizing risks of any regulatory fees and penalties.

Real-World Applications of PETs

There are privacy-preserving technologies that have been employed to transform the workflows of key industries worldwide. 

This allows them to securely exchange information, develop predictive machine learning algorithms, and analyze cross-border operational trends without compromising any regulatory compliance or personal user data: 

• Collaborative Healthcare Research: Hospitals make use of synthetic patient data and federated learning to train diagnostic algorithms using data from patients of different hospitals without breaching any patient privacy regulations.
• Multi-Bank Fraud Detection: Banks employ the technique of secure multi-party computation to detect money laundering networks involving multiple banks without disclosing the account information of customers.  
• Demographic Census Reporting: Governments make use of differential privacy techniques in the publication of national censuses, ensuring detailed public statistical insights remain completely accessible to researchers without ever risking the re-identification of individual household records.

Conclusion

With digital processes becoming more connected, using outdated security techniques increases privacy vulnerabilities. Implementing privacy-enhancing technology enables companies to strike a good balance between analytics and data protection.

Through the use of technologies such as synthetic data, differential privacy, and homomorphic encryption, progressive companies create trust among consumers while innovating through data safely.

Frequently Asked Questions