What is a Privacy-Enhancing Technology: All You Need To Know About PETs

Privacy-Enhancing Technology
Find Source

Although cybersecurity and PETs may appear to be similar at first look, they are actually quite distinct. While cybersecurity is focused on preventing unauthorized individuals from accessing the data, Privacy-Enhancing Technology, in data analysis, focuses on protecting the privacy of the data. In other words, cybersecurity prohibits access to certain data (not all) for analysis, in order to derive useful information. Both, however, are crucial for making sure that data is well-protected and complies with the data protection law.

It’s crucial for businesses in all sectors, including adtech, fintech, life sciences, and health care, to be able to share data among themselves to provide analytics and insights. Clean rooms are one application where homomorphic encryption is employed in the real world. In clean rooms, which feature tight privacy rules that forbid access to consumers’ individually identifying information, marketers and publishers can aggregate user data from many platforms and combine it with first-party advertiser data for measurement and attribution.

What are PETs?

PET is the technology that protects your privacy and the confidentiality of your data is referred to as privacy-enhancing technology. PETs can be used to speed up the processing of sensitive data while increasing the use of the data for both internal and external enterprise projects.

Synthetic data produced by AI
Find Source

Main Types of PETs

Many companies were able to better protect their data thanks to PETs. However, because of how quickly technology is developing, you frequently need a mix of different PETs rather than a single, stand-alone solution.

A thorough list of PETs that may be useful for your data projects is provided below. Among the various PET varieties are:

  • both in-transit and at-rest encryption
  • Techniques for removing identity include tokenization and k-anonymity.
  • Pseudonymization
  • Synthetic data produced by AI
  • Analysis using homomorphic encryption is encrypted
  • dependable execution settings (TEE)
  • Secure multi-party computation and federated analytics are examples of anonymous computing.
  • Individual privacy

Leave a Reply

Your email address will not be published. Required fields are marked *