In recent years, differential privacy has emerged as a promising solution for enhancing privacy protections in data processing systems. However, beneath its seemingly robust framework lie certain assumptions that, if left unquestioned, could inadvertently undermine its efficacy in safeguarding individual privacy.

Here to discuss their recent papers on differential privacy is Rachel Cummings, Associate Professor of Industrial Engineering and Operations Research at Columbia University and CDT Non-Resident Fellow and Daniel Susser, Associate Professor for the Department of Information Science at Cornell University and CDT Non-Resident Fellow.