On the Challenges of Deploying Privacy-Preserving Synthetic Data in the Enterprise
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Abstract (arXiv)
Generative AI technologies are gaining unprecedented popularity, causing a mix of excitement and apprehension through their remarkable capabilities. In this paper, we study the challenges associated with deploying synthetic data, a subfield of Generative AI. Our focus centers on enterprise deployment, with an emphasis on privacy concerns caused by the vast amount of personal and highly sensitive data. We identify 40+ challenges and systematize them into five main groups -- i) generation, ii) infrastructure & architecture, iii) governance, iv) compliance & regulation, and v) adoption. Additionally, we discuss a strategic and systematic approach that enterprises can employ to effectively address the challenges and achieve their goals by establishing trust in the implemented solutions.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 20 of its 83 references that are in the map. First posted on arXiv 9 Jul 2023.
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20 of 83 references.
References · 20
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8 more
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