Data breaches cost businesses, on average, $4.9 million per incident. This also comes with reputation loss and decreased consumer trust. Privacy Enhancing Technologies (PETs) can help businesses avoid data breaches and their consequences. Therefore, it's wiser to invest in PETs than wait for data breaches to occur. As regulations tighten, PETs are becoming essential for ensuring compliance and maintaining consumer trust.

Sometimes referred to as privacy-preserving technologies or data protection technologies, PETs encompass methods such as encryption, anonymization, access controls, federated learning, and other techniques.

Let's explore various privacy-enhancing technologies.

What Are Privacy Enhancing Technologies (PETs)?

Privacy Enhancing Technologies (PETs) are tools and techniques designed to protect personal data by minimizing, securing, or eliminating the collection and processing of Personally Identifiable Information (PII). They enable organizations and individuals to use data responsibly while complying with privacy laws, such as GDPR and CPRA, as well as other global regulations. 

PETs help reduce privacy risks by:

• Limiting data collection.
• Anonymizing or pseudonymizing Personal Information.
• Allowing data analysis without revealing personal data.

Types of Privacy-Enhancing Technologies

There are PETs for privacy-preserving data collection and for privacy-preserving data processing.

Businesses should use data privacy by design and default principles and implement privacy-enhancing technologies for data collection to comply with data privacy regulations.

PETs for privacy-preserving data processing are cryptographic techniques that allow sensitive data to be analyzed, shared, or used in computations without exposing the actual data. These PETs are essential in industries like healthcare, finance, advertising, and research, where data is highly sensitive and highly regulated by privacy laws, including GDPR, CPRA, and HIPAA.

Several different types of PETs enable businesses to obtain and process the data they need while maintaining data privacy.  

Data Minimization Techniques

Data minimization tools limit the amount of data collected to only what is strictly necessary. Minimizing data collection and controlling access to data helps to limit the potential for unauthorized access to personal information.

Examples of data minimization techniques include collecting aggregated device information instead of precise geo-targeted data, or recording user behavior in categories rather than exact actions.

Anonymization & Pseudonymization

These technologies hide personally identifying information from datasets to reduce re-identification risk.

Anonymization irreversibly removes identifying data so individuals can't be re-identified.

Pseudonymization replaces identifying data with reversible tokens or pseudonyms.

If done correctly, anonymized and pseudonymized data does not need user consent under GDPR.

Data Derivation PETs

These PETs weaken the link between the identities of individuals and their data.

Differential Privacy

This data aggregation method that adds randomized noise to the data, so that data cannot be reverse-engineered to reveal the original inputs. Apple, Google, and Microsoft use it for analytics. Differential Privacy enables useful information about a population without revealing individual user behaviors.

However, not all noise-adding techniques lead to differential privacy. Differential privacy is specifically about determining the precise amount of noise needed to achieve statistical privacy assurances.

Federated Learning

Federated Learning a machine learning technique where multiple devices or entities collaboratively train a shared model without directly exchanging their raw data. The technique allows statistical analysis or model training on decentralized data sets or locally, rather than centralizing raw data, so sensitive data never leaves the device. The model gets smarter with every analysis of the data. It is used in mobile keyboard personalization, fraud detection, IoT devices, etc.

Secure Multi-Party Computation (SMPC)

SMPC allows multiple parties to jointly compute functions over their inputs without revealing their own data. It ensures input privacy so that no single party ever sees Personal Information from other parties.

Businesses can collaborate on shared market analyses pooling their data together while maintaining the confidentiality of their customers’ datasets.

Zero-Knowledge Proofs (ZKPs)

A zero-knowledge proof methodology is a protocol in which one party (the prover) can convince another party (the verifier) that some given statement is true, without conveying to the verifier any information beyond the mere fact of that statement's truth. ZKP is used for identity verification, authentication, and as an anti-fraud tool. For example, website users can prove that they are over 18 years old or confirm that a user is a real person and not a bot, without revealing any personal data. No personal data is sent to the verified, so the method greatly reduces data exposure.

Trusted Execution Environments (TEEs)

TEE is a secure area within a main processor. Sensitive data operations are isolated in secure areas of the processor. Unauthorized entities from outside the TEE can’t access that data, while code integrity prevents code in the TEE from being replaced or modified by unauthorized entities.

Homomorphic Encryption (HE)

Homomorphic Encryption enables computation on encrypted data without needing to decrypt it first. This allows data to be encrypted and outsourced to commercial cloud environments for processing, encrypted analytics, or outsourced processing.

For example, in healthcare, predictive analytics service providers could operate on encrypted data without having the decryption keys. The prediction of a disease could be performed without compromising sensitive medical data.

​In digital advertising, homomorphic encryption enables advertisers to analyze encrypted user data without accessing personal information. This allows for the delivery of personalized ads without revealing user privacy.

Private Set Intersection (PSI)

Private Set Intersection enables two parties to compute the intersection of their datasets without revealing any personal data. PSI allows two parties to compare datasets and identify common elements without revealing the full data. Apple uses this technique in Password Monitoring. It has proposed using this technology for its announced Expanded Protections for Children.

PSI could be used in audience matching, contact tracing, marketing collaborations, etc. In the server-client scenario, only the client learns the intersection of its set with the server's set, without the server learning the intersection of its set with the clients.

Synthetic Data

It’s fully algorithmically generated data produced by a computer simulation that approximates a real personal data set. Reducing the usage of sensitive data from individuals in highly regulated industries allows for software testing without non-compliance risks.

Differentiating between types of PETs is crucial, as their effectiveness and maturity vary based on their type and use cases.

Benefits of Privacy-Enhancing Technologies

With the rapid growth of digital marketing and analytics, businesses collect, share, and process an unprecedented amount of personal data. This creates new opportunities, but also significant risks, such as data leaks, data breaches, financial expenses, and reputational losses.

Under the GDPR, fines can reach up to €20 million or 4% of global annual revenue — whichever is higher. The CPRA allows the California Privacy Protection Agency to issue penalties of up to $7,500 per intentional violation, including those involving minors’ data.

Privacy-enhancing technologies are essential for mitigating these risks, enabling secure data processing, and ensuring compliance with privacy regulations.

Privacy-enhancing technologies are crucial for individuals and organizations.

Benefits of PETs for individuals

PETs have the following benefits for individuals:

• Prevention of Data Breaches
  Data breaches could pose the most significant threats to personal privacy. When data is exposed, individuals can become victims of financial theft, fraud, or identity theft. PETs help minimize the risk of unauthorized access to sensitive data, reducing the impact of potential breaches.
• Enhanced Privacy
  PETs help individuals control how their personal data is used, ensuring that they keep their data safe and protect their identities. Without PETs, individuals are at risk of having their data exposed, leading to identity theft, fraud, or unwanted surveillance.
• Increased Trust in Digital Services
  The growing concern over privacy issues has an enormous effect on individuals' decisions to use digital platforms. By using PETs, businesses can demonstrate their commitment to protecting customer data, fostering greater trust. This is especially important when consumers have to reveal their sensitive information like health data.
• Increased Online Experiences
  When data privacy is ensured, individuals can enjoy more personalized and engaging online experiences without sacrificing their privacy. By implementing PETs, businesses can deliver tailored services and products without compromising user privacy.

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Benefits of PETs for organizations

• Compliance with Privacy Regulations
  Data privacy laws such as GDPR or CPRA are evolving, and new regulations are constantly emerging. PETs are crucial for ensuring compliance with current and emerging privacy laws.
• Data breach prevention
  Data breaches could have significant effects not only on personal privacy but also on business reputation, trust, and financial consequences. By implementing PETs, businesses can prevent data breaches and avoid penalties for non-compliance with privacy laws.
• Data misuse prevention
  Another risk for companies handling personal data is data misuse. By applying encryption, homomorphic encryption, and other privacy-enabling techniques, organizations can ensure that data is only used for its intended purpose and by authorized parties only.
• Secure data collaboration
  Organizations often need to collaborate with third parties, such as partners, vendors, or service providers to share their data. This poses a risk of data leak or misuse. PETs enable secure data collaboration and promote innovation without exposing sensitive information. PETs such as a private set intersection or federated learning, for example, enable multiple parties to collaborate on data analysis without revealing their customers’ data.
• Data minimization
  One of the key principles of privacy regulations like the GDPR is data minimization, which requires that only the minimum amount of data necessary for a specific purpose should be collected and processed. PETs enable organizations to work with data more efficiently and securely, without storing or processing unnecessary personal information.
• Innovation and Data Use
  PETs allow organizations to innovate and improve services by utilizing data for analysis and insights without sacrificing privacy. By implementing PETs, organizations can employ new opportunities for innovation while complying with privacy standards.
• Reduced Financial Consequences
  PETs help mitigate this risk by ensuring that data is handled securely and in compliance with privacy laws, protecting businesses from costly lawsuits, fines, and reputational harm.

The Future of Privacy-Enhancing Technologies

While privacy regulations continue to evolve, the requirements for Privacy-Enhancing Technologies are also increasing. These are the future trends for privacy-preserving data collection and for privacy-preserving data processing:

• Combination of PETs for layered protection.
• Wider adoption of open-source PET libraries (e.g., OpenMined, Microsoft SEAL).
• Server-side data collection with built-in minimization logic.
• Built-in consent-aware logic, where data capture is gated based on real-time consent signals.
• PETs + AI integration to allow training on sensitive datasets (e.g., medical images) without compromising patient privacy.
• Integration into Consent Management Platforms (CMPs) for real-time enforcement of privacy rules during data collection and processing.

The Future of Consent

As privacy regulations and privacy-enhancing technologies evolve, the traditional model of cookie banners and binary consent is becoming outdated. It’s not enough to provide users with just two choices of accepting or rejecting cookies.

The future of consent is based on the following aspects:

• Contextual and Granular Consent
  Provide users with more control over what types of data they share and with whom. Granular consent should be your default choice when thinking about consent and cookie management.
  Expect consent interfaces that offer purpose-based or category-based controls.
  
• Consent as a Continuous Process
  Consent will not be a one-time pop-up but a dynamic, ongoing dialogue. When the purpose of collecting personal data changes, the consent should also change accordingly.
  Real-time consent withdrawal and update mechanisms are becoming standard.
• Privacy by Design & Default
  Consent should be initially embedded into product and service design. Implement PETs in building systems that require no consent at all by avoiding personal data collection altogether.
  
• Regulatory Push & Interoperability
  Frameworks like Global Privacy Control and Consent Receipt Standards require to make consent portable and interoperable across services.
  
• AI-Driven Consent Management
  Consent records are increasingly managed by AI and audit-ready systems to reflect real-time changes in user choices and transparency.
  Smart, AI-based consent management platforms can adapt to user preferences and automate compliance.

How Can CookieScript Help You to Manage Consent?

A professional Consent Management Platform (CMP) is an essential PET that can enhance your data protection efforts, create transparency around the data collection and management practices, and achieve compliance.

CookieScript CMP is a robust CMP that helps businesses collect, store, and manage user consent data to comply with privacy laws like the GDPR and CPRA. It allows businesses to set granular consent options for data subjects, scan websites for cookies and update Cookie Consent in real time, when new cookies are found.

CookieScript CMP helps businesses to create privacy laws-compliant Privacy Policy and updates it automatically.

In addition, CookieScript CMP offers the following functionalities:

• CookieScript is a Google-certified CMP, included in the list of certified partners.
• CookieScript received GOLD Tier in the new Google Tiering System.
• GDPR-compliant consent mechanism.
• Explicit and granular Cookie Consent options.
• Full consent banner customization.
• Powerful Cookie Scanner, detecting cookies, local storage, session storage, and other website trackers.
• Integration with Google Consent Mode v2.
• Integration with IAB TCF v2.2.

In 2025, CookieScript received the fourth badge in a row as the leader on G2, a peer review site, and became the best CMP on the market for a whole year! 

Frequently Asked Questions

What are privacy-enhancing technologies?

Privacy Enhancing Technologies (PETs) are tools and techniques designed to protect personal data by minimizing, securing, or eliminating the collection and processing of personally identifiable information (PII). They maximize data security and enable marketers to gain campaign insights, analyze audience data, and optimize their reach.

What are the benefits of privacy-enhancing technologies?

PETs help individuals control how their personal data is used, prevent form data breaches, and increase trust in digital services, ensuring that they keep their data safe and protect their identities. They help organizations comply with privacy regulations, prevent data misuse, and allow secure data collaboration. CookieScript CMP is a crucial PET for compliance with privacy laws.

What do privacy-enhancing technologies do?

Privacy-enhancing technologies (PETs) allow safe data usage by reducing risks inherent to data leaks, frauds, and misuses. Some PETs provide tools for anonymization, while others enable collaboration on privately held personal data. CookieScript CMP is a crucial component for compliance with privacy laws.

What are the emerging technologies for privacy?

Emerging privacy technologies include homomorphic encryption, secure multi-party computation, zero-knowledge proofs, differential privacy, and trusted execution environments. These privacy-enhancing technologies allow sensitive data to be analyzed, shared, or used in computations without exposing actual data. CookieScript CMP is a robust PET for compliance with privacy laws.

How can AI improve privacy?

AI can enhance privacy by automating data protection processes and detecting potential privacy breaches. For example, AI algorithms can identify uncommon behavior and patterns that may indicate unauthorized access.