On the Challenges of Deploying Privacy-Preserving Synthetic Data in the Enterprise

Lauren Arthur, Jason Costello, Jonathan Hardy, Will O'Brien, James Rea +2 more
7/9/2023
cs.LGcs.AIcs.CR

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Abstract

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.

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Cite this paper

@article{arthur2023challenges,
  title  = {On the Challenges of Deploying Privacy-Preserving Synthetic Data in the Enterprise},
  author = {Lauren Arthur and Jason Costello and Jonathan Hardy and Will O'Brien and James Rea and Gareth Rees and Georgi Ganev},
  year   = {2023},
  eprint = {2307.04208},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2307.04208v1}
}

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