Elevate Security with Privacy-Enhanced Technologies
Protect sensitive data and maintain compliance with our cutting-edge privacy-enhanced technologies. Designed to secure your operations, these solutions allow for seamless data verification and collaboration without compromising user privacy. Stay ahead of regulatory demands and build trust with your customers by integrating advanced privacy safeguards into your business processes.
Understanding Privacy Enhancing Technologies (PETs)
Privacy Enhancing Technologies (PETs) protect the privacy of individuals and organisations while ensuring data confidentiality. PETs minimise the use of sensitive information, enabling secure data processing and analysis. Key techniques include homomorphic encryption, anonymization, zero-knowledge proofs, and multiparty computation, allowing organisations to leverage data effectively while maintaining strict privacy standards.
Encryption
Ensures that only authorised parties can access or decrypt it, protecting sensitive information from unauthorized access or interception.
Secure Multiparty Computation
Secure Multiparty Computation (MPC) allows multiple parties to jointly compute a function over their inputs without revealing individual inputs to each other, protecting the privacy of sensitive data.
Anonymization
Removes or masks identifying information from datasets to prevent individuals from being personally identifiable while still allowing for data analysis.
Homomorphic Encryption
Allowing computation on encrypted data without decrypting it, preserving the confidentiality of data throughout processing.
Tokenization
Substitutes sensitive data with a non-sensitive equivalent (token) that has no exploitable value, preserving privacy while maintaining usability.
Privacy-preserving Data Minding
Techniques that enable analysis of data while protecting individual privacy, such as federated learning, where models are trained on distributed datasets without sharing raw data.
Differential Privacy
Adds noise to statistical queries or data to prevent the identification of individual data points, ensuring that aggregate results remain accurate but individual data remains private.
Zero-knowledge Proofs
Techniques for proving the validity of a statement or claim without revealing any information beyond the fact that the statement is true.
Exploring Different Types of Privacy Enhancing Technologies (PETs)
Privacy Enhancing Technologies (PETs) include tools like homomorphic encryption, anonymization, zero-knowledge proofs, and multiparty computation. These technologies help organisations protect sensitive data, ensure compliance, and maintain user trust during secure data processing.
FAQs
Here's how you can get more information on PETs: Look for research papers from reputable sources like IEEE, ACM, or Google Scholar. Keywords like “Privacy Enhancing Technologies,” “PETs,” “data privacy,” or “data anonymization” will yield solid research materials. Websites & Blogs like EPIC (Electronic Privacy Information Center), The Electronic Frontier Foundation (EFF), World Economic Forum & OECD Governments and institutions like the European Union Agency for Cybersecurity (ENISA) or National Institute of Standards and Technology (NIST) publish extensive resources and guides on PETs. For example, ENISA’s reports on privacy-by-design or data anonymization offer in-depth analysis of the technologies involved. If you're interested in specific applications of PETs (such as in finance, healthcare, or machine learning), many industries have tailored reports and guides such as ISO/IEC 29100 and GDPR Guidelines Books like "Privacy-Preserving Machine Learning" and "Privacy Enhancing Technologies: The Path to Privacy-by-Design" explore the technical depth of PETs.
Healthcare Data Sharing and Research Problem: Healthcare data, including medical records and genetic information, is highly sensitive, but it is also invaluable for research purposes such as drug development, epidemiological studies, and personalised medicine. PET Solution: Homomorphic Encryption allows researchers to perform calculations on encrypted data without ever seeing the underlying information, ensuring that patient data remains confidential. Differential Privacy adds noise to data sets, preventing identification of individuals while still enabling useful statistical analysis. This is particularly useful for healthcare institutions sharing aggregated data for research without violating patient privacy. Financial Transactions and Anti-Money Laundering (AML) Problem: Financial institutions are required to share customer data for anti-money laundering (AML) and fraud detection. However, this involves exchanging sensitive financial information, which can lead to privacy risks. PET Solution: Secure Multi-Party Computation (SMPC) enables multiple parties (e.g., banks, regulators) to collaborate and analyse financial transactions without exposing their individual customer data. They can jointly compute whether any transactions are suspicious without revealing the details of unrelated customers. Zero-Knowledge Proofs (ZKP) allow one party to prove that a transaction is valid or compliant without revealing any underlying financial details.
Privacy-Enhancing Technologies (PETs) are increasingly used across a wide range of industries, helping organisations balance the need to leverage data with strict privacy and security requirements. Here’s where PETs are being actively implemented today: Healthcare, Finance and Banking, Digital Advertising and Marketing, Telecommunications and Internet of Things (IoT), Government and Public Sector, Cloud Computing and Data Analytics, Retail and E-Commerce, Blockchain and Cryptocurrencies, Voting Systems, Academic and Collaborative Research.
The cost of implementing and running Privacy-Enhancing Technologies (PETs) can vary significantly depending on the specific technology, the scale of the deployment, and the complexity of the use case. Basic PETs (e.g., encryption, anonymization) are generally low-cost to implement and run, as they are widely available and often built into many existing platforms. Advanced PETs (e.g., homomorphic encryption, secure multi-party computation) can be expensive due to high computational requirements, increased storage and bandwidth needs, and the cost of specialised expertise. Cost Efficiency: Managed cloud services and open-source solutions can make PETs more affordable, and the cost is often balanced against the benefits of regulatory compliance, data protection, and long-term savings from avoiding data breaches or legal penalties.
The difficulty of implementing Privacy-Enhancing Technologies (PETs) depends on several factors, including the type of PET, the complexity of the system it’s being integrated into, the organisation’s technical expertise, and the specific use case. Some PETs are relatively easy to adopt, while others require advanced knowledge, specialised skills, and significant computational resources.
Still have questions?
Project PHACKS
The Platform for High Assurance Collaborative Knowledge Sharing (PHACKS) is an EU funded commercialisation initiative aimed at developing new markets for advanced privacy-preserving technologies where data collaboration is at the core of the participants collaboration. The project focuses on creating solutions that enable secure and privacy-respecting access to digital services and resources that leverage data assets that each of the participants hold but in a manner where those assets are not disclosed to the other participants. PHACKS leverages cutting-edge cryptographic techniques, including zero-knowledge proofs and secure multiparty computation, to ensure that sensitive information is completely protected during the data processing stages.
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