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All You Need to Know about Privacy-Enhancing Technologies

All You Need to Know about Privacy-Enhancing Technologies

  • Post published:September 16, 2022

Are you keen to know about privacy-enhancing technologies? Do you know the importance and benefits of these technologies?

Continue reading to figure out what privacy technologies are. What benefits do they have to offer and how can you choose the best among the rest?

Whether you have to maximise the data utility or remain compliant with privacy regulations, enhanced privacy is pertinent to ensure smooth operations. We can help you to maintain your data operations eloquently.

What are Privacy-Enhancing Technologies (PETs)?

PETs cover multiple methods to assist you in protecting privacy and data confidentiality throughout its processing time. It is productive to secure or streamline sensitive data processing to maximise data utility through external or internal projects.

Privacy Enhancing Technologies respects the following data protection principles:

  • Fairness and Transparency
  • Accuracy
  • Accountability
  • Storage limitation
  • Data minimization
  • Purpose limitation
  • Integrity and confidentiality
  • Lawfulness

How do PETs get popular?

PETs are not famous before introducing ad-blocks, anonymization, Tor networks, synthetic data, and pseudonymization. It has been functional in the business for the past decades but gained popularity after raising awareness around security and privacy compliance.

What is the proper time to utilise Privacy-Enhancing Technologies?

PETs are required to run an enormous data project that is involved with a third party but don’t want to show your data publicly. Multiple PETs will assist you with your data with an additional level of protection.

How is PET functional?

“Using PEC enables people to collaborate and collect data without sharing information with any other party.”


PETs in general are a compilation of different technologies that safeguard and deliver additional protection during search and analytics performance.

Here’s how it works

Do you know why PECs are essential for different organisations? PECs protect the privacy rights of consumers. A user is concerned before submitting his details on a website whether it’s protected through PECs or not. In addition, multiple enterprises lack a proper tested process and offer an effective way to exploit information to gain control.

Also Read: Data Fabric Reduces Management Hassles

Having no PECs can affect user privacy, the reputation of the company, and the confidence individuals have about the actions to give to an organisation.

Privacy-enhancing technologies to protect data

Everyone is familiar with the privacy issues i.e., data protection in those companies that collect data from the users and are afraid of catastrophic data breaches.

Following are the multiple privacy-enhancing technologies to protect data:

On-device learning

Without sending the information of the users, the device is capable to analyze the user behaviour and identify a pattern. On-device learning is effective to improve algorithmic intelligence through the process of auto-correction.

For example: If you are familiar with the Apple ID, you will get to know that it enables users to utilise a machine-learning algorithm to collect data about how their face looks, and it assists users more precisely.

Federated learning

Federated learning is a subcategory of machine learning in which technology maintains the device’s memory to learn an underlying prediction model through data sharing and retaining locally to the system.

Federated learning is in demand while changing mobile devices and maintaining their updates. It also minimises the storage space requirements from cloud storage systems or central servers.

Synthetic Data Generation (SDG)

SDGs deliver artificial raw data through identical statistical attributes, as it sets to produce greater than the original sets of data. This technique is helpful to adopt both for test environments and AI applications.

Generative Adversarial Networks (GANs)

Generative Adversarial Networks, also known as GANs, are programmed to generate suppositional instances of data that helps to run data sets. It allows analytical research to obtain high-level synthesis information from the computer. These networks utilise quick identity anomalies on the internet to question medical test results.

Types of PETs

Technology advancement is rapid, and now it is productive to maintain the right path for many organisations to protect their data. Following is the list of multiple PETs that are helpful in data projects.

  1. Trusted execution environments (TEE)
  2. De-identification techniques: tokenization or k-anonymity
  3. Differential privacy
  4. Anonymized computing: secure multi-party
  5. Encrypted analysis: homomorphic encryption
  6. AI-generated synthetic data
  7. Pseudonymization
  8. Encryption in transit and at rest

How to choose suitable PETs for your project?

  • Follow the below-mentioned tips to choose suitable data protection.
  • Differentiate or identify a specific type of data between structured and unstructured you are using
  • Think about whether you have to share information with third parties or not
  • Define whether you demand access to the dataset or only the output
  • To train machine learning and artificial intelligence applications, decide whether data is required or not.
  • Evaluate your budget (a few PETs cost a hefty amount of money)
  • Evaluate your computation power (PETs demand advanced infrastructure)
  • Examine whether personally identifiable information is required to be kept in the dataset or not

Final Thoughts

“By 2025, half of the large organisations will implement privacy-enhancing technologies for processing data in untrusted environments and multiparty data analytics use cases.”


Early adoption of technology for business will lead you to tremendous value from data collaboration. It charges high in the collaboration of medical and banking industries. Privacy computing technologies turn more provocative in performance and improved broader adoption to be expected.