ListOT and PCF
Here are my Slides. The design of PCF follows two paradigms in general. The “DPF+LPN” approach, where one uses binary-tree-based DPF as FSS for point functions for comparison functions to generate correlations on sparse vectors (e.g. sparse COT, sparse VOLE), and then compress the correlation with an appropriate Learning Parity with Noise (LPN) assumption. Notice that we want to achieve super-polynomial “stretch” so that the LPN assumption must support a “fast” evaluation algorithm....