How do Privacy-Enhancing Technologies protect privacy while using data?

In today’s digital world, data has become extremely valuable for organizations. Companies use personal data to improve their services, offer personalized experiences, prevent fraud, and make better decisions. At the same time, people are more worried than ever about how their personal data is collected, used, shared, and protected. News about data breaches, misuse of information, and increased monitoring has made privacy a major global concern.

This is where Privacy-Enhancing Technologies (PETs) become important. PETs are tools and methods that help protect personal data while still allowing organizations to use it effectively. They help ensure that privacy and innovation can work together, rather than forcing organizations to choose one over the other.

What are Privacy-Enhancing Technologies (PETs)?

Privacy-Enhancing Technologies, or PETs, are tools and technical methods that help reduce the amount of personal data used, limit who can see it, and lower privacy risks when data is being processed. Their main goal is to protect people’s privacy from the beginning, instead of depending only on policies, contracts, or manual checks.

PETs also support important privacy principles like collecting only what is needed, using data only for specific purposes, and being responsible and transparent. These principles are strongly highlighted in laws such as the General Data Protection Regulation and India’s Digital Personal Data Protection Act, 2023.

Why PETs Are Becoming Essential

Several reasons are leading to the growing use of Privacy-Enhancing Technologies (PETs)

Stricter data protection laws
Data protection laws around the world now impose heavy fines for collecting or using personal data incorrectly. Organizations must prove that they are following the rules, not just say that they are compliant.

More data sharing and collaboration
Companies often need to share data with different teams, business partners, cloud service providers, and even across countries. PETs make it possible to share data safely while reducing privacy risks.

Growing awareness among individuals
People are becoming more aware of their privacy rights and expect clear information and control over how their personal data is used. Building trust has become important for businesses.

Growth of analytics and artificial intelligence
AI and machine learning depend on large amounts of data. PETs allow organizations to use data for analysis and innovation while protecting people’s identities.

Key Types of Privacy-Enhancing Technologies

Some of the most common and important PETs are explained below.

1. Data Masking and Anonymization

Data masking means hiding or changing sensitive information such as names, phone numbers, or ID numbers. The data can still be used for testing or analysis, but the real details are not visible.

Anonymization completely removes personal details so that a person can no longer be identified. When done properly, anonymized data is usually not covered by data protection laws.

However, anonymization must be done carefully. If it is weak, people may still be identified by combining the data with other information.

Example of Data Masking

A company is testing a new customer management system. Instead of using real customer information, sensitive details are hidden or altered.

Original data:

• Name: Ramesh Kumar
• Phone number: 9876543210
• Aadhaar number: 1234-5678-9012

Masked data:

• Name: XXXXX Kumar
• Phone number: 98XXXX3210
• Aadhaar number: XXXX-XXXX-9012

This allows the team to test the system effectively without seeing full personal details, reducing the risk of data misuse during testing.

2. Pseudonymization

Pseudonymization replaces personal details with fake names or codes. The real identity is stored separately and securely.

Unlike anonymization, the data can still be linked back to a person if needed. This method is encouraged under the General Data Protection Regulation because it lowers privacy risk while allowing data to be used for valid business purposes. For example, hospitals can study patient data without revealing real identities.

Example of Pseudonymization - A bank wants to study customer spending patterns to strengthen its fraud detection system.

Original data:

• Name: Rajesh Mehta
• Account number: 4567890123
• Transaction history

After pseudonymization:

• Customer ID: CUST-88921
• Transaction history

The link between CUST-88921 and Rajesh Mehta is kept in a separate, secure system that only authorized bank staff can access.

This allows data analysts to examine spending patterns and identify fraud without knowing the customer’s real identity. When needed for customer service or regulatory purposes, the bank can reconnect the data to the individual. This approach lowers privacy risk while still supporting legitimate business needs.

3. Encryption (At Rest, In Transit, and In Use)

Encryption protects data by converting it into a form that cannot be read without a key.

• Encryption at rest protects stored data.
• Encryption in transit protects data while it is being sent.
• Encryption in use protects data even while it is being processed.

Encryption greatly reduces the damage caused by data breaches and is often required by law.

Example of Encryption at Rest:

When customer data is stored in a company’s database or on a hard drive, it is encrypted. Even if someone gains unauthorized access to the storage system, they cannot read the data without the encryption key.

Example of Encryption in Transit

When you log in to your online banking account, your username and password travel from your computer or mobile phone to the bank’s server.

Encryption protects this information while it is being sent over the internet. Even if someone tries to intercept the data during transmission, they will not be able to read or use it because it is converted into a secure, unreadable form.

Example of Encryption in Use

When sensitive data is being processed in a secure area, it stays encrypted the whole time. This means the system can use the data for calculations without ever showing it in a readable form.

4. Differential Privacy

Differential privacy adds a small amount of controlled noise to data or results. This makes it difficult to identify individuals while still keeping overall trends accurate.

It is commonly used in large studies, surveys, and data analysis, allowing insights without revealing personal information.

Example of Differential Privacy - A city conducts a health survey to understand diabetes trends. Instead of showing exact numbers, a small amount of random data is added to the results.

For example, an actual count of 10,000 people may be shown as 9,980 or 10,020. The overall trend remains accurate, but individual responses cannot be identified, helping protect privacy.

5. Secure Multi-Party Computation (SMPC)

Secure Multi-Party Computation enables multiple organizations to collaborate on calculations without sharing their data.

Example of Secure Multi-Party Computation - Banks can identify fraud patterns together without revealing customer details to each other. This is useful in sectors where data is sensitive, but cooperation is needed.

6. Federated Learning

Federated learning allows machine learning models to be trained where the data is stored, instead of moving data to a central location.

Only the results or updates are shared, not the raw data. This reduces data movement and improves privacy, especially in AI systems.

7. Privacy-preserving identity and access control

Some modern identity systems use PETs like zero-knowledge proofs. These allow users to prove something about themselves, such as being above a certain age, without sharing extra personal details. This helps follow the principle of using only the minimum data required and gives users more control over their information.

PETs and Privacy by Design

Privacy-Enhancing Technologies help organizations apply privacy by design and by default, an approach strongly supported by the General Data Protection Regulation and promoted by bodies such as the European Union and the International Organization for Standardization.

Instead of adding privacy controls later, PETs build privacy directly into systems, processes, and ways of working from the start. This helps organizations lower compliance costs, avoid making changes later, and build stronger trust with individuals.

Challenges in Implementing PETs

Even though Privacy-Enhancing Technologies offer many benefits, they also come with some challenges:

Technical complexity
Some PETs are difficult to implement and need skilled experts and advanced systems.

Impact on performance
Certain privacy techniques may slow down systems or affect accuracy.

Cost concerns
Setting up and maintaining PETs can be costly for organizations.

Limited awareness
Many organizations are still not fully aware of what PETs are or how they can be used.

With proper planning, staff training, and a risk-based approach, these challenges can be managed effectively.

Conclusion

Privacy-Enhancing Technologies (PETs) represent a pivotal shift in how organizations safeguard personal data. Rather than restricting data usage, PETs enable responsible utilization while minimizing privacy risks and embedding protection from the outset.

As data becomes key to innovation and business growth, Privacy-Enhancing Technologies (PETs) help organizations use data responsibly while protecting people’s privacy. By using PETs, organizations can keep personal information safe, follow data protection laws, and build long-term trust in the digital world.

Key Takeaways

1. PETs protect privacy while using data
Privacy-Enhancing Technologies allow organizations to use data for business and innovation without exposing personal information.

2. Privacy is built into systems from the start
PETs support privacy by design, meaning privacy protection is added early, not as an afterthought.

3. PETs help meet data protection laws
Using PETs makes it easier for organizations to comply with data protection regulations and avoid penalties.

4. Different PETs serve different purposes
Techniques such as masking, encryption, differential privacy, and federated learning protect data in different ways and under different circumstances.

5. Trust and responsible data use are the biggest benefits
By using PETs, organizations can reduce risks, protect individuals, and build long-term trust in the digital ecosystem.