Genomic Data Analysis in Life Sciences with Privacy-Enhancing Technologies

A leading life sciences firm safeguards PHI, ensuring full HIPAA and GDPR compliance for genomic data analysis and research.

cut in data preparation and compliance time
estimated breach risk reduction
boost in data usability
Case Study Summary
A life sciences company, positioned at the forefront of personalized medicine and genomic research, addressed the challenges of safeguarding Patient Health Information (PHI) and genomic data.

Through the adoption of Anonos Data Embassy and the implementation of statutory pseudonymization techniques, the company achieved HIPAA and GDPR compliance, minimized data breach risks, and ensured the security and privacy of its data assets for the genomic data analysis project.
  • Genomic data is inherently sensitive. The company's research, which aimed at exploring genetic markers for specific diseases and tailoring drug regimens, leveraged large volumes of such data.
  • With the increasing HIPAA and GDPR regulatory scrutiny, ensuring the privacy of this data became paramount. Traditional data protection techniques, both cumbersome and not fully compliant with legal requirements, posed limitations in achieving research objectives without compromising patient privacy.

Solution: Variant Twins Facilitate Minimum Necessary Data

The life sciences company tested Anonos Data Embassy solution, to create a holistic approach to PHI, genomic data privacy and adhere to data minimization requirements.

  • Under U.S. HIPAA, data minimization is known as the Minimum Necessary Data Rule: Limiting the use or disclosure of, and requests for, protected health information to the minimum necessary to accomplish the intended purpose.
  • Under the GDPR, data minimization is a foundational principle, emphasizing that organizations should only collect, process, and store personal data that is adequate, relevant, and strictly necessary for their specific purpose.
Using Data Embassy, the company employed statutory pseudonymization, a GDPR-approved methodology where selected fields within the data records are replaced with artificial identifiers, ensuring the records were less identifiable yet remained suitable for genomic data analysis and processing.
A diagram of Variant Twins technology, demonstrating the process of separating data value from personally-identifiable information through record-level dynamic pseudonymization to ensure insurance data privacy.
Through Data Embassy, the raw genomic data underwent a transformation into Variant Twins, protected transformations of original data. These statutorily pseudonymized replicas maintained the analytical value of the original data while bolstering medical data privacy.
An illustration demonstrating what Anonos Variant Twin is and how it safeguards sensitive customer data for various insurance data use cases.
Variant Twins enabled the sharing of information pertaining to successive degrees of precision related to identifying the relationship between a subject’s phenotype (e.g., disease state) and genotype (their DNA) by revealing just the level of identifying information necessary at each level for authorized use.

Variant Twin (VT) 1
Pathways bearing mutations and subjects in binary cohort groups
Variant Twin (VT) 2
VT1 + Genes bearing mutations and detailed disease classification
Variant Twin (VT) 3
VT2 + Specific gene variants and disease class scores
Variant Twin (VT) 4
VT3 + Hapmap haplotype results and full disease history
Variant Twin (VT) 5
VT 4 + Full SNP data and full patient record
Variant Twins
Enhanced Data Security With the advanced data privacy techniques in place, the risk of potential data breaches was reduced by an estimated 80%, saving the company potential losses and penalties that could run into millions.
Operational Efficiency GDPR pseudonymization streamlined the data processing workflow, leading to a 60% reduction in time spent on data preparation and compliance checks, accelerating the research and saving man-hours.
Preserved Data Utility The company could utilize over 95% of its data for genomic data analysis without compromising on individual privacy, compared to the previous 60% with conventional methods.
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