Abstract 

The general topic of this talk is approximating a uniform distribution on a compact metric space. As an example, consider a binary code   and a function . We say that is a noise kernel if it acts on by convolution, and attempt to estimate the proximity of the (normalized) image to the uniform distribution on using norms. Some new estimates for this approximation are derived, and applications to information hiding are considered. Based on a joint work with Madhura Pathegama.