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

• URL: http://arxiv.org/abs/2307.04208v1
• Date: Sun, 9 Jul 2023 15:42:22 GMT
• Title: On the Challenges of Deploying Privacy-Preserving Synthetic Data in the Enterprise
• Authors: Lauren Arthur, Jason Costello, Jonathan Hardy, Will O'Brien, James Rea, Gareth Rees, Georgi Ganev
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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