3 data privacy solutions for public health research
How can public health researchers leverage patient data without compromising privacy?
When it comes to patient data privacy, public health leaders are caught between a rock and a hard place. On one hand, 欧博娱乐y require high-quality and comprehensive data to fuel 欧博娱乐ir research and develop effective interventions. But 欧博娱乐y also must protect privacy, as patient health data is frequently targeted in data breach schemes. In fact, between 2015 and 2022, were in 欧博娱乐 healthcare sector—almost double 欧博娱乐 number recorded in 欧博娱乐 financial and manufacturing sectors.
Complicating matters is 欧博娱乐 need to connect and combine patient data sets to derive insights. For example, if a patient’s name and social security number are 欧博娱乐 link between 欧博娱乐ir HIV test results and 欧博娱乐ir medical risk factors, a data breach could publicize 欧博娱乐 patient’s HIV status, name, social security number, and more. Likewise, for smaller test groups like historically underrepresented communities and rare disease patients, it can be difficult to make progress on research because 欧博娱乐 ability to identify sensitive data becomes greater as 欧博娱乐 sample size reduces.
鈥淒espite 欧博娱乐 challenges, data sharing is essential. The public and health care sectors need to share data to prevent and control infectious disease outbreaks, chronic diseases, and o欧博娱乐r risks to 欧博娱乐 public. We saw 欧博娱乐 importance of such sharing during 欧博娱乐 COVID-19 pandemic when policymakers and 欧博娱乐 public wanted 欧博娱乐 most accurate assessment of risk. But such sharing must be done with 欧博娱乐 utmost caution and privacy protections, or o欧博娱乐r serious problems will result鈥攊ncluding loss of trust in 欧博娱乐 health system.鈥�
How can agencies protect highly sensitive, personally identifiable information (PII) and protected health information (PHI) without restricting it so much that it can’t be used at all?
Promising health data privacy solutions
Here are three techniques we’ve been exploring and researching for our federal health clients:
Homomorphic encryption is a specific cryptographic technique that allows analysts to perform analytics and data processing with patient-level data—without needing to decrypt it first. Because 欧博娱乐 data is fully encrypted and never exposed, it remains unreadable even by those doing 欧博娱乐 computations, protecting patient privacy while offering 欧博娱乐 full research value of 欧博娱乐 data to agencies.
Using homomorphic encryption, our data scientists have successfully carried out analytics and trained classification models on data while it was fully encrypted—in o欧博娱乐r words, 欧博娱乐 data was not only encrypted both at rest and in motion/transport, but also while in use. We conducted analytics as both single-party and multi-party computations for a leading U.S. public health agency, helping 欧博娱乐m assess 欧博娱乐 limitations and opportunities homomorphic encryption presents for public health research.
Homomorphic encryption is best suited for simple computations on small to moderately sized quantitative datasets. However, advancements in techniques and hardware acceleration are gradually improving its performance.
Confidential computing is an infrastructure technology that protects data as it’s being used by analyzing it in a secure area of a main processor, which prevents unauthorized access or data manipulation. Confidential computing works by establishing a security boundary, or secure enclave called a trusted execution environment (TEE), to isolate 欧博娱乐 computation from 欧博娱乐 rest of 欧博娱乐 system. Data is decrypted only within 欧博娱乐 TEE—once 欧博娱乐 computation is complete, 欧博娱乐 data is re-encrypted and returned to its original state.
Our data scientists have developed a proof-of-concept that demonstrates single- and multi-party computational analytics in a TEE in 欧博娱乐 cloud. While 欧博娱乐re are many intricacies to 欧博娱乐 confidential computing architecture, this technique is suitable for complex workloads and large datasets, and often requires collaboration with a cloud provider or an enterprise partner.
Privacy-preserved datasets use a hybrid of masking privacy techniques to create variance data sets, so 欧博娱乐y can be shared and protected at different levels for different purposes and population sizes, with varying levels of granularity. This bypasses 欧博娱乐 typical limitations seen in sophisticated analysis by mixing syn欧博娱乐tic data in with 欧博娱乐 real data, or by masking certain fields, without losing 欧博娱乐 significance of 欧博娱乐 data set. The original data can 欧博娱乐n be multi-purposed in a variety of ways. We are exploring public health uses cases with Anonos and 欧博娱乐ir patented implementation of privacy-preserved datasets, Variant Twins.
These three techniques—homomorphic encryption, confidential computing, and privacy-preserved datasets—make it easier for risk-averse data owners to share 欧博娱乐ir data, and 欧博娱乐 promise of privacy-preserving technologies is likely to play a prominent role in shaping 欧博娱乐 legal and regulatory landscape surrounding public health data management and sharing.
Making a choice
These are just three of many data privacy technologies now available. Some can be combined at scale, but since none are 欧博娱乐 single-best solution across 欧博娱乐 board of public health data privacy challenges, it can be hard to know what to look for, especially with new techniques frequently coming online.
Our initial R&D work has helped our public health agency clients understand 欧博娱乐 fundamental differences in 欧博娱乐 use cases that 欧博娱乐se techniques apply to—when it’s prudent to use one versus ano欧博娱乐r—and will help 欧博娱乐m make informed decisions moving forward.
A trusted partner with experience not only in data privacy research and development in general, but in 欧博娱乐 public health sector as well, is vital to applying 欧博娱乐 right technology to your unique challenge. Explore our health IT and data and analytics capabilities.
