On the Challenges of Deploying Privacy-Preserving Synthetic Data in the Enterprise
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.
Keywords
Cite
@article{arxiv.2307.04208,
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},
journal= {arXiv preprint arXiv:2307.04208},
year = {2023}
}
Comments
Accepted to the 1st Workshop on Challenges in Deployable Generative AI, part of ICML 2023