Small and Medium sized Businesses (SMBs) make up majority of employment in Africa (around 80%). Understanding the digital transformation that SMBs in Africa went through during the pandemic can play an important role in uncovering how to build solutions that better support the African business and African worker. In this paper we report on findings from a qualitative study with 40 SMBs in Kenya. The study aimed to understand the lived experience of digital transformation, the impacts of COVID-19 on their businesses, and how they responded to such impacts using technology. We found that COVID-prompted digital transformation was reactive and opportunistic, plus social and collective. Moreover, the socialness of business goes way beyond digital transformation, and influences how SMBs in Kenya start, develop and are sustained. In illustrating this, we offer a lens to understanding work and workers of SMBs in Kenya and similar contexts across the globe.

In this paper, we investigate the effect of machine learning based anonymization on anomalous subgroup preservation. In particular, we train a binary classifier to discover the most anomalous subgroup in a dataset by maximizing the bias between the groups predicted odds ratio from the model and observed odds ratio from the data. We then perform anonymization using a variational autoencoder (VAE) to synthesize an entirely new dataset that would ideally be drawn from the distribution of the original data. We repeat the anomalous subgroup discovery task on the new data and compare it to what was identified pre-anonymization. We evaluated our approach using publicly available datasets from the financial industry. Our evaluation confirmed that the approach was able to produce synthetic datasets that preserved a high level of subgroup differentiation as identified initially in the original dataset. Such a distinction was maintained while having distinctly different records between the synthetic and original dataset. Finally, we packed the above end-to-end process into what we call Utility Guaranteed Deep Privacy (UGDP) system. UGDP can be easily extended to onboard alternative generative approaches such as GANs to synthesize tabular data.