The value of privacy-enhancing technologies for businesses in 2025
Global privacy regulations are becoming stricter every year, and keeping up can be exhausting for marketers. What was considered safe last year may not be compliant today, and falling behind risks customer trust and costly fines.
Delegating some of these concerns to privacy-preserving technologies can help. While many tools support global compliance requirements, privacy-enhancing technologies (PETs) stand out. They enable responsible data processing that places user trust at the center of compliance.
In this guide, you’ll learn what privacy-enhancing technologies are, how they work, and how to determine their value for your business in 2025.
Key takeaways
- Privacy-enhancing technologies (PETs) help businesses comply with the GDPR, CCPA, and other global privacy laws.
- PETs support users’ trust by safeguarding sensitive data through encryption, anonymization, federated analytics, and other methods.
- Major companies already use PETs to balance data privacy with business needs.
- PETs provide benefits such as supporting regulatory compliance, cost savings, and building consumer trust.
- PETs still have limitations in scalability, cost, and data utility, and layering multiple methods is recommended.
- Businesses should adopt PETs within a privacy by design framework, aligning them with strategy, infrastructure, and user experience.
Why privacy-enhancing technologies matter in 2025
Data privacy-enhancing technologies are becoming critical tools in organizations’ comprehensive data security systems. They help companies keep pace with the compliance requirements of fast-changing regulations and rising consumer expectations.
In 2025, PETs play a central role in building consistent, comprehensive data protection systems. They apply principles such as data minimization, which is increasingly critical as marketing compliance requirements grow stricter.
Regulations like the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), the Digital Markets Act (DMA), and the EU AI Act now apply across different jurisdictions and address different areas of business and technology. Each has unique requirements.
For marketers, complying with all relevant laws, frameworks, and policies can be daunting. When businesses fall behind, they face increased risks of noncompliant data handling or data breaches that erode trust.
Generative AI adoption further complicates the landscape as AI and data privacy concerns rise. An IBM report found that 63 percent of companies lack proper governance for AI-based security systems, exposing both customer and employee data to risk.
PETs help mitigate risk by enabling data to be transmitted, stored, and shared securely, in line with global privacy regulations.
What are privacy-enhancing technologies?
Before examining categories and use cases, it is important to understand what PETs actually are. Privacy-enhancing technologies are tools and methods designed to protect data across its lifecycle — from collection to storage to processing and transmission.
These data privacy technology tools reduce the risks of data breaches, strengthen customer trust, and support business sustainability by aligning with regulatory requirements and user expectations.
Categories of PETs
PETs can be classified into distinct categories, each targeting a specific aspect of data protection. According to the Organization for Economic Cooperation and Development (OECD), PETs can be grouped into four main categories:
- Data obfuscation
- Encrypted data processing tools
- Federated and distributed analytics
- Data accountability
Each category contributes to compliance and secure data use across industries, from research and healthcare to marketing analytics.
| Category | Key technologies | Examples of current and potential application |
| Data obfuscation | Anonymization or pseudonymizationSynthetic data Differential privacyZero-knowledge proofs | Expanding research opportunitiesSecure storagePrivacy-preserving machine learning (ML)Verifying information with no disclosure (e.g. age verification) |
| Encrypted data processing | Homomorphic encryptionTrusted execution environmentsMulti-party computation | Computing using models that need to remain privateEncrypted data computing within the same organizationContact discoveryComputing on private data that is too sensitive to disclose |
| Federated and distributed analytics | Federated learningDistributed analytics | Privacy-preserving ML |
| Data accountability | Threshold secret sharing Accountable systemsPersonal information management systems | Providing data subjects control over their own data Immutable data access tracking by data controllersSetting and enforcing the rules on when the data can be accessed |
Types of privacy-enhancing technologies
The UK Information Commissioner’s Office (ICO) has identified four key types of PETs that address different privacy needs — including compliance and user experience — and contribute to a privacy by design ecosystem.
Privacy-enhancing technologies for data minimization and security
This data privacy technology type makes personal data less identifiable, helping to limit the risks of unauthorized access. These PETs use mechanisms like data minimization, anonymization, encryption, and access controls.
Privacy-enhancing technologies for data derivation
Data derivation PETs weaken the connection between data from a person’s input and their identity. This makes them effective for helping to handle data exposure risks, though added noise can impact data utility in certain scenarios.
Privacy-enhancing technologies for data splitting and access control
Data splitting and access control PETs assist in personal data management within systems. These technologies help to preserve data integrity and confidentiality by splitting datasets for storage or analysis and using specialized hardware to limit data access.
Privacy-enhancing technologies for data hiding and shielding
Data hiding and shielding PET techniques include homomorphic encryption and zero-knowledge proofs. Homomorphic encryption enables encrypted data to be analyzed without revealing the underlying plaintext. Zero-knowledge proofs help verify the truths without disclosing the underlying data or extra information.
Regulatory perspectives on PETs
Data privacy technology is closely tied to achieving and maintaining regulatory compliance, and helps organizations align with legal requirements across multiple jurisdictions.
The GDPR and privacy-enhancing technologies
GDPR principles emphasize privacy by design, which includes limits on data collected and protecting it at every stage of processing, among other principles. PETs help businesses address:
- Data minimization (Art. 5 GDPR)
- Integrity and confidentiality (Art. 5 GDPR)
- Technical and organizational measures (Art. 25 GDPR)
Read more about the recent GDPR implications for B2B sales.
CCPA and privacy-enhancing technologies
The CCPA emphasizes consumer rights, including disclosure, removal, and restrictions on data sharing. PETs such as consent management platforms (CMPs) support granular opt-in vs. opt-out choices, helping businesses meet these requirements.
Privacy-enhancing technologies examples in 2025: Company experiences
The need for PET integration continues to grow. Over 60 percent of large businesses worldwide are expected to have integrated at least one PET solution in their data security systems by the end of 2025. Their data privacy technology sets will include tools used in business intelligence, analytics, and cloud computing.
Roche and PETs: Fully Homomorphic Encryption (FHE)
Roche uses fully homomorphic encryption to analyze encrypted patient data from laboratories without decryption. This shielding helps secure data sharing and analysis and supports GDPR compliance and protects patient privacy.
Upon implementation, Roche’s operations data is encrypted so that laboratories cannot access its proprietary algorithms. PETs enabled Roche to support compliant research collaborations with simplified security procedures. This approach also helped reduce the risk of data breaches, which had an average cost of USD 4.88 million in 2024.
Google and PETs: Federated Learning and Differential Privacy
Google uses two privacy-enhancing technologies — federated learning and differential privacy — to analyze data across devices without centralizing users’ raw data.
Google uses federated learning in its Gboard keyboard to improve predictive text. This enables user data to stay on on individual devices, and only model updates are sent to Google’s servers, not specific keystrokes.
Differential privacy is a tool that adds statistical noise to aggregated data, limiting the risk of identifying individual users. This way, data can be shared with less risk of exposure or consent violations. A critical improvement when regulatory fines under the GDPR can reach up to four percent of global annual revenue or EUR 20 million per infraction.
For Google and its global reach, these privacy-enhancing techniques help safeguard user privacy while still supporting key revenue drivers, including search, Google Assistant, and personalized advertising.
Microsoft and PETs: Confidential Computing
Microsoft migrated Windows licensing to Azure Confidential Computing in 2025, so businesses can run analytics or AI workloads without revealing sensitive data to the cloud provider or unauthorized users.
This update helped to boost data security and support regulatory compliance, which in turn builds trust with customers demanding secure, scalable cloud services.
Benefits of privacy-enhancing technologies (PETs) for organizations
By supporting regulatory compliance and helping to maintain customer trust, adopting data privacy technology has several tangible benefits.
- User data protection: Privacy-enhancing technologies support data minimization principles, which reduces risks related to personal data processing and storage and supports privacy compliance.
- Secular, granular data sharing: Privacy-preserving technologies enable implementation of granular access controls, limiting third-party access to authorized users.
- Cost effectiveness: By minimizing the risk of data breaches and other privacy violations, privacy-enhancing technologies help companies save money and resource allocation in responding to issues and maintaining complex tech ecosystems.
Stronger of consumer trust: A majority of Americans believe there should be more governmental regulation over how businesses can handle user data. To earn and maintain trust, businesses must demonstrate respect for data and user privacy and provide clear choices, as with data privacy technologies.
Challenges and limitations of PETs
PETs should not be the only tool in companies’ data privacy compliance strategy. A more sustainable approach is to make privacy-enhancing technologies part of a comprehensive system that supports data privacy every stage handling.
PETs are not infallible
Privacy-preserving technologies should complement other security approaches, not replace them. Organizations should integrate multiple security layers into business processes for more comprehensive protection that scales as companies grow.
PETs can affect data utility
Privacy-enhancing technologies can reduce the utility of the collected data, which can affect marketing and other business processes. Companies need to balance data security with business objectives that rely on high-quality data. The right balance requires a data minimization approach across operations.
PETs may conflict with user privacy rights
PETs can conflict with user privacy rights. For instance, anonymization techniques can protect data from misuse, but they shouldn’t prevent individuals from exercising rights, like access or deletion. Business leaders should invest in data privacy technologies that uphold privacy principles and balance operational requirements and user expectations.
PETs may be resource-intensive
Depending on the technology used, investing in PETs can be costly in time, money, and human resources. Some data-preserving technologies, like consent management platforms, can be seamlessly integrated within your tech stack. Others, like federated learning and homomorphic encryption, may require new infrastructure, specialized expertise, or even refactoring existing systems.
Best practices for adopting privacy-enhancing technologies
PETs enable you to achieve data-driven results without compromising privacy. Adopting them effectively requires a privacy by design approach.
- Make privacy by design a core value: Create and implement a holistic privacy strategy and integrate it into all stages of planning and development.
- Choose relevant PETs for your privacy needs: Options include consent management platforms, data anonymization tools, data encryption technologies, and more. Aim to implement a combination of PETs for layered privacy.
- Design for better user experience: Prioritize usability and transparency as you implement technologies and clearly communicate the changes with your users.
- Ensure PETs align with your business goals: Establish clear, measurable objectives like improved consent rates, secure AI model training, or reduced data exposure.
- Invest in cross-functional training: Equip data-using teams across the company to collaborate on PET initiatives so everyone understands and works to make data privacy a competitive advantage.
Key factors in selecting PETs
To minimize risk of issues with PET implementation, businesses need to choose a data privacy technology that works for their industry, regulatory requirements, business operations and goals, and user expectations.
Here are a few important questions to ask to help you choose a PET for your privacy compliance needs:
- Does it help you comply with current relevant regulatory requirements and partners’ policies, and adapt to legal changes?
- Does it align with your current tech infrastructure (analytics, AI/ML, cloud storage etc.)?
- Does it contribute to improved user experience?
- Does it have robust documentation and timely support for when you need them?
Privacy-enhancing technologies: AI and future trends
Tools like federated learning and automated consent management already use AI-based systems and machine learning (ML) models to support streamlined privacy compliance. In the future, privacy-enhancing technologies with these innovations will continue to become more integrated into digital ecosystems and enable greater protections at scale.
Some potential trends for AI-driven privacy-enhancing technologies include:
- AI-powered privacy automation: AI-driven PETs will increasingly automate consent management and privacy compliance, adapting in real-time to evolving regulations, policies, and user preferences.
- Enhanced risk detection: AI models integrated with PETs can help detect anomalies and mitigate potential privacy risks early, especially in complex, distributed data environments.
- Multi-layered privacy protection: Combining AI with PET methods like differential privacy and encryption supports strong, flexible safeguards without sacrificing data utility.
- Better privacy and preference management: Transparent, AI-enabled privacy controls can better use consent and preference signals from customers for enhanced user experience and demonstration of your privacy commitment.
How Usercentrics supports privacy-enhancing strategies
The Usercentrics Consent Management Platform (CMP) is one data privacy technology that helps businesses achieve privacy compliance and show your customers that you respect their data and privacy.
The Usercentrics CMP prioritizes privacy compliance while balancing business needs for high-quality data and consumers demands for control.