Paper 2019/264
Unifying computational entropies via Kullback-Leibler divergence
Rohit Agrawal and Yi-Hsiu Chen and Thibaut Horel and Salil Vadhan
Abstract
We introduce KL-hardness, a new notion of hardness for search problems which on the one hand is satisfied by all one-way functions and on the other hand implies both next-block pseudoentropy and inaccessible-entropy, two forms of computational entropy used in recent constructions of pseudorandom generators and statistically hiding commitment schemes, respectively. Thus, KL-hardness unifies the latter two notions of computational entropy and sheds light on the apparent "duality" between them. Additionally, it yields a more modular and illuminating proof that one-way functions imply next-block inaccessible entropy, similar in structure to the proof that one-way functions imply next-block pseudoentropy (Vadhan and Zheng, STOC '12).
Metadata
- Available format(s)
- Category
- Foundations
- Publication info
- Preprint. MINOR revision.
- Keywords
- one-way functionspseudo-randomnessbit commitmentinformation theorypseudoentropyinaccessible entropy
- Contact author(s)
- thorel @ seas harvard edu
- History
- 2019-08-20: revised
- 2019-03-06: received
- See all versions
- Short URL
- https://ia.cr/2019/264
- License
-
CC BY