The Challenge of Patient Data Privacy

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Healthcare providers and pharmaceutical companies face the dual challenge of protecting patient data while extracting meaningful insights for research and development. Conventional anonymization often degrades data quality and also leaves it vulnerable to re-identification. Research by Latanya Sweeney and others shows that supposedly anonymized datasets can be re-identified using public available records. The need for robust data privacy solutions is critical, especially with increasing data-driven decision-making and the upcoming AI era in healthcare. Traditional methods fail to ensure privacy and data usability simultaneously, so analyzing, collaboration and data sharing for example between clinics and healthcare companies is usually impossible.
For a deeper analysis, more examples and more specific use cases than in this blog post, download our medical whitepaper.

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Tabularis Technology: A New Solution Using Artificial Data Conversion

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Tabularis uses transformer-based language models (LLMs) to generate hyper-realistic artificial tabular data based on the original, sensitive data. In general, Tabularis technology allows an LLM to efficiently abstract all distributions and correlations of a sensitive data set and then sample a completely new data set that reflects all learned correlations but does not contain or reveal any of the original sensitive data points. This method addresses challenges like lossy preprocessing and re-identification risks of anonymization while ensuring best data quality. The process involves:

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A Real-World Case Study

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To demonstrate our artificial data’s efficacy, we compared a primary healthcare dataset with its artificial version generated by Tabularis. This showcases how Tabularis maintains the statistical properties and correlations, ensuring the artificial data is as usable for analysis as the primary data while ensuring impossible re-identification and therefor full privacy.
To highlight the similarities between the primary and our artificial dataset, we show visual representations of the comparison. In the following you see Boxplots representing distributions of three Parameters in comparison and a correlation Matrix showing the difference of correlations between both datasets.

Benefits

• Data Privacy and Legal Compliance: Ensures complete privacy and compliance with GDPR and HIPAA by eliminating connections to real individuals. Remove the personal information in a legally valid form.
• Preservation of Statistical Integrity: Retains distributions, correlations, and variances, making artificial data perfect for analysis and machine learning.
• Cost-Effective Data Management and Risk Mitigation: Reduces costs and risks associated with sensible data preparation and handling.
• Enables Free Data Sharing: Allows Collaboration on high quality data through legal Compliance.

Summary

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Tabularis offers a powerful, secure, and efficient way to leverage healthcare data. By ensuring full privacy and preserving statistical integrity, it enables comprehensive use without compromising patient privacy. Tabularis artificial data can drive innovation and improve patient outcomes by enabling safe and effective data use and sharing. For a deeper analysis, more examples and more specific use cases, download our medical whitepaper.

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Next Steps

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