Privacy-enhancing technologies (PETs) are computational methods and security frameworks that enable organizations to analyze and share sensitive data while protecting confidential information. These approaches are increasingly important in laboratory environments that manage clinical research records, biotechnology data assets, proprietary workflows, and regulated digital information. To accelerate real-world PET deployment, the US National Science Foundation Directorate for Technology, Innovation and Partnerships (NSF TIP), in partnership with NSF Directorate for Computer and Information Science and Engineering (NSF CISE) and several federal and industry collaborators, has awarded $10.4 million over three years to 10 research teams through the Privacy-Preserving Data Sharing in Practice (NSF PDaSP) initiative.
The initiative focuses on practical, scalable solutions that enable secure data sharing and analytics across sectors such as healthcare, agriculture, transportation, cybersecurity, and biotechnology. For laboratory leaders, these capabilities may support multi-site research collaborations, privacy-preserving analytics on sensitive datasets, and stronger governance practices in regulated data environments.
How privacy-enhancing technologies support secure analytics
The funded projects emphasize industry collaboration, community test beds, and deployment-ready tools. Teams will apply several core PET approaches to advance secure data sharing and analysis without exposing raw datasets.
Key PET techniques and how they work:
- Federated learning: A distributed machine-learning method that trains models across multiple data holders while keeping original datasets stored locally; each site contributes model updates rather than transferring underlying records
- Secure multiparty computation: Cryptographic protocols that allow institutions to generate joint analytical results while keeping individual inputs confidential
- Differential privacy: A statistical technique that introduces controlled noise into outputs so aggregate insights can be shared without revealing identifiable information
- Trusted execution environments: Hardware-isolated secure computing enclaves that reduce exposure risk by separating sensitive workloads from the broader system
These technologies allow laboratories and partner organizations to analyze clinical, genomic, environmental, materials, or operational datasets while maintaining security controls and minimizing privacy risk.
Program goals and strategic context
According to Erwin Gianchandani, the assistant director for NSF TIP, “NSF is prioritizing investments in critical technologies to ensure a reliable and practical AI future while at the same time positioning the US as a global leader in AI. The outcomes from these NSF investments will empower government agencies and private industry to adopt leading-edge PETs and harness the power and insights of data for the public good.”
The program responds to rapid growth in computational capacity and data-intensive research, where organizations require technologies that are both deployable and scalable. By pairing research and development with real-world testing environments, the PDaSP initiative supports the transition from conceptual PET models to production-level systems suited for mission-critical applications in national security, biotechnology, and advanced computing.
What this means for laboratory managers
Laboratories increasingly participate in distributed research networks, cross-institutional analytics programs, and multi-partner data collaborations. Privacy-enhancing technologies may help labs:
- Support secure data sharing across research sites
- Protect participant privacy in clinical and translational studies
- Reduce cybersecurity and governance risk exposure
- Enable AI and analytics workflows on regulated datasets
- Align collaboration practices with institutional and regulatory expectations
Operational adoption may require planning in areas such as infrastructure readiness, vendor tool integration, workforce training, and data stewardship policy alignment.
Positioning labs for future adoption
The PDaSP initiative forms part of NSF TIP’s broader portfolio supporting use-inspired and translational research, as well as workforce development in key technology domains. As privacy-enhancing technologies continue to mature, laboratories may gain new opportunities to collaborate, analyze sensitive datasets, and accelerate discovery while safeguarding data privacy and security across research operations.
This article was created with the assistance of Generative AI and has undergone editorial review before publishing.
