PETs are approaches that allow companies to move towards the latter end of the spectrum – some reach the end-goal of a company truly not having access to the data of any individual, and others reside in the middle, where they limit access but still have some reliance on a company’s policies and practices. The technology used was a Trusted Execution Environment (TEE), specifically Intel SGX, to securely process uniformly hashed IMSIs through the Sharemind HI platform. Limited disclosure technology provides a way of protecting individuals’ privacy by allowing them to share only enough personal information with service providers to complete an interaction or transaction. Organisations should be able to understand their digital performance while maintaining full ownership and control of their data.
Pseudonymization is frequently used by businesses to comply with GDPR. Obfuscation is a data masking technique that complicates data to impede reverse-engineering while preserving functionality for authorized users. Indonesia’s Ministry of Tourism aimed to generate accurate tourism statistics from mobile roaming data while addressing privacy concerns when sharing datasets from two mobile network operators (MNOs).
For instance, in healthcare, anonymization allows researchers to study trends without risking patient confidentiality. Operating on the principle of “never trust, always verify,” ZTA ensures that every access request — regardless of origin — is authenticated, authorized, and continuously validated. PETs are essential for safeguarding data and maintaining user trust https://mamemame.info/practical-and-helpful-tips-14/ in digital environments. For example, encryption converts data into a coded format readable only by authorized users, while anonymization removes identifiable information from datasets.
Synthetic data
Privacy-enhancing technologies (PETs) are technologies, tools, techniques, and practices designed to protect individuals’ privacy. Privacy-enhancing technologies (PET) are technologies that embody fundamental data protection principles by minimizing personal data use, maximizing data security, and empowering individuals. Privacy Enhancing Technologies (PETs) are tools that protect personal data while allowing organisations to process information responsibly. Beyond federated learning, a broader suite of AI and machine learning techniques is reshaping how organizations approach data protection at every layer of their security stack.
Financial Services
Depending on the data clean room provider, they will employ different combinations of PETs to build data clean rooms with privacy-preserving capabilities. These are secure environments where organizations can safely collaborate on or share data while it stays protected. Decentriq’s data clean rooms, for instance, leverage confidential computing and operate within isolated secure enclaves. Organizations use PETs to meet data privacy regulations like GDPR and CCPA while maintaining operational efficiency. This is particularly valuable in sectors like finance, defense, or telecommunications where data breaches can have national or global implications. In practice, organizations are increasingly turning to PETs to collaborate on threat intelligence and behavioral analytics without sharing raw data—especially when that data includes confidential logs or user information.
As lawmakers, platforms, and regulators increasingly converge on age assurance as a governance tool, the updated infographic sharpens the focus on proportionality, privacy … This blog post outlines the Commission’s emerging strategic approach to the implementation of EU-wide age verification measures, provides an … Building on FPF’s 2025 landscape analysis of PETs adoption by State Education Agencies, the new resources move from landscape analysis to implementation considerations — providing audience-specific guidance for the three practitioner communities most … The answer to this question could be one of the keys to unlock trusted cross-border data flows at scale in the age of AI. FPF also facilitates a Global PETs Network of regulators who are interested in the lawful and ethical implementation of these tools. Science in the age of AI explores how AI is transforming the methods and nature of scientific…
Business Guidance
However, it must ensure compliance with legal standards and protect against re-identification risks when linking datasets from different organizations, adhering to national laws and the European GDPR. Some privacy enhancing technologies are also data masking techniques that are used by businesses to protect sensitive information in their data sets. The cryptographic algorithm adds a statistical noise6 layer to the dataset which enables to describe patterns of groups within the dataset while maintaining the privacy of individuals.
- This is vital not only for protecting individual rights but also for earning and maintaining public trust in technologies that use data about people.
- When data privacy is ensured, individuals can enjoy more personalized and engaging online experiences without sacrificing their privacy.
- Privacy Enhancing Technologies (PETs) are a suite of tools that can help maximise the use of data by reducing risks inherent to data use.
- Their goal is to allow the user of an app or website to communicate with a company’s servers without the company learning who they are.
One specific area where PETs are quite useful is data collaboration. Matomo aligns with global privacy laws such as GDPR, CCPA, HIPAA, LGPD and PECR. Purpose limitation means organisations use data solely for the intended purpose and don’t share or sell it to third parties. Security and confidentiality protect sensitive data against inappropriate access.
Encryption and Data Anonymization
- Hospitals worldwide contribute model updates from their local datasets to build a global model without sharing patient scans.
- On the other end of the spectrum, there are technologies which allow a company to offer products and services without ever having access to a user’s data.
- Read more about privacy-preserving technology and its ability to make real-world healthcare data available for collaboration that results in improved patient outcomes.
- Swiss banks used confidential computing from Decentriq to collaborate and gain insights into cyberthreats.
As AI systems increasingly rely on vast amounts of data, PETs like differential privacy, homomorphic encryption, federated learning, zero-knowledge proofs, and even model contract clauses can help ensure that personal data remains confidential and secure. The growing concern over privacy issues has made trust a key factor in individuals’ decisions to use digital platforms and services. Secure multiparty computation and federated learning, for example, enable multiple parties to collaborate on data analysis without revealing their individual datasets. When the census data is made publicly available, the noise ensures that no one can use the data to identify specific households or individuals.
- These tools are challenging to shortlist individually due to their vast diversity.
- Your analytics data is protected by globally recognised security standards.
- The CARRIER project uses secondary processing of medical, lifestyle, and personal data to estimate risks and enable early detection and intervention for coronary artery disease.
- Pseudonymisation works differently from synthetic data, though both help protect individual privacy.
- For example, secure multi-party computation can enable multiple companies to jointly analyze attack patterns across their networks without ever revealing the underlying data to one another.
Federated learning
For example, several hospitals can collaborate on research without sharing patient records. Despite these advantages, homomorphic encryption can be complex to compute and take longer than other traditional methods. It is most useful in financial services, where analysts need to protect sensitive customer data and secure transactions. For example, https://4equality.info/getting-down-to-basics-with-30/ under GDPR, organisations may face fines of up to €20 million or 4% of their global annual revenue for serious violations. However, collecting and processing personal data can lead to privacy concerns, regulatory challenges, and reputational risks. Let’s continue to evolve our methods and stay ahead in the ever-changing landscape of data security.
Enveil ZeroReveal®
SMPC enables joint computations on private data without revealing the raw data. Homomorphic encryption is a cryptographic technique in which users can perform calculations on cipher text without decrypting it first. In industries like healthcare, finance and marketing, businesses often need detailed analytics to improve operations and target audiences effectively. This highlights the need for organisations to prioritise user privacy. By leveraging encryption, anonymization, Federated Learning, and Secure Multiparty Computation, we can significantly bolster our data security frameworks. Integrating Privacy-Enhancing Technologies is crucial for safeguarding sensitive data and ensuring regulatory compliance.