2. Privacy-enhancing technologies
WEF Emerging Technology 2024
Protecting personal privacy while providing new opportunities for global data sharing and collaboration, “synthetic data” is set to transform how information is handled with powerful applications in health-related research.
Competitive Environment in privacy-enhancing technologies
Companies and universities with conceptually close patents to the technology definition
Countries in technology
(number of active patents in technology in 2024 country by inventor address)
Development of patent publications
(publications per year)
EconSight comment and short analysis
Personal and Data Privacy is a huge topic at the interface of data benefit (using more and better data can help developing better solutions) and personal privacy. This is a particular challenge in health and medical data, but also in many other areas with sensitive personal data. The field is dominated by large IT players, such as IBM, Microsoft or Alphabet, Huawei, SAP or Amazon, mostly because of their huge resources for the high computer demanding solutions, such as homomorphic encryption, but we can see also Health related players, such Philips, Siemens or Roche. Remarkably some unexpected players, such as Capital One and Mastercard have also found the topic highly relevant to position themselves and are rather close to the search concepts used with their FinTech approaches. A significant number of smaller players is also active in the field, often focusing on Health and Patient Data related services and solutions. We see a slightly lower amount of university patents (10% of total patents), which is unusual for an area of such important developments, but this indicates either a rather competitive commercial market environment or a too generic search concept.
Background
- Textual concepts are generated for each WEF technology.
- The concepts are applied to our full-text AI-RAG (retrieval augmented generation system) which is optimised for highest precision patent analysis to identify the semantically close patents for each technology.
- A competitive environment with the most relevant companies and research institutions is developed where the relevance is calculated as the similarity of their patents compared to the technology concept. The closer to the technology core, the higher the similarity. Large corporates, small specialists and research institutions are shown separately.
Privacy-enhancing technologies: Protecting personal privacy while providing new opportunities for global data sharing and collaboration, “synthetic data” is set to transform how information is handled with powerful applications in health-related research.
This invention relates to a method for generating synthetic data that preserves the privacy of individuals while enabling large-scale health-related research. The method utilizes advanced machine learning algorithms to create synthetic datasets that statistically mirror real-world health data without compromising personal information. The synthetic data is designed to maintain the utility for research and analysis while ensuring compliance with global privacy regulations. This technology enables researchers to collaborate across borders and access diverse data sources without risking data breaches or privacy violations.
The present invention describes a system for secure global data sharing, specifically in the context of health-related research. The system combines homomorphic encryption with synthetic data generation techniques to allow researchers to perform computations on encrypted data without revealing sensitive personal information. The synthetic data generated by the system is used for collaborative analysis, ensuring that privacy is maintained throughout the data sharing process. This system provides a robust solution for organizations needing to balance data privacy with the need for global collaboration.
This invention provides a framework for enabling cross-border collaboration in health-related research by generating privacy-preserving synthetic data. The framework incorporates differential privacy techniques to ensure that the synthetic data does not reveal any personal information from the original datasets. The synthetic data retains the statistical properties necessary for research while being fully compliant with international privacy standards. This technology allows researchers from different countries to collaborate effectively without the risk of data privacy breaches.
The invention relates to a method for generating synthetic data that can be used for developing predictive models in healthcare while ensuring the privacy of individuals. The method employs generative adversarial networks (GANs) to produce synthetic datasets that are indistinguishable from real-world data but do not contain any identifiable information. This approach enables healthcare organizations to develop and test predictive models without accessing or exposing sensitive patient data, facilitating innovation in health research while maintaining strict privacy standards.
This patent describes an automated system for the anonymization and synthesis of health-related data, aimed at protecting personal privacy while enabling research and data sharing. The system uses a combination of data anonymization techniques and synthetic data generation to create datasets that are both useful for research and secure from a privacy perspective. The system is designed to be scalable, allowing for the processing of large datasets commonly used in health research, and supports compliance with various international privacy regulations.
Further information on our analysis approach and how we identify the most exciting startups and newcomers in highly specialised technology domains and evaluate them for private equity and venture capital can be found in our Precision Investing approach.
