Harnessing Generative Language Models for Data Synthesis in Biostatistics: a Blockchain-Based Approach

EasyChair Preprint 12552

Abstract

In the realm of biostatistics, the synthesis of data is crucial for various research endeavors, from epidemiological studies to clinical trials. However, ensuring the privacy and integrity of sensitive health data poses significant challenges. This paper proposes a novel approach that harnesses generative language models, coupled with blockchain technology, to address these challenges. By leveraging the capabilities of generative language models, such as GPT (Generative Pre-trained Transformer) models, data synthesis can be conducted while preserving privacy and maintaining statistical integrity. Additionally, the utilization of blockchain ensures secure and transparent data transactions, enhancing trust in the synthesized data. This paper discusses the theoretical framework, implementation methodology, and potential applications of this blockchain-based approach in biostatistics.

Keyphrases: Biostatistics, Blockchain Technology, Generative Language Models, Privacy, Security, data synthesis

@booklet{EasyChair:12552,
  author    = {William Jack},
  title     = {Harnessing Generative Language Models for Data Synthesis in Biostatistics: a Blockchain-Based Approach},
  howpublished = {EasyChair Preprint 12552},
  year      = {EasyChair, 2024}}