  We put forth a new computational notion of entropy, measuring the
(in)feasibility of sampling high-entropy strings that are consistent with a
given generator. Specifically, the i'th output block of a generator G has
accessible entropy at most k if the following holds: when conditioning on its
prior coin tosses, no polynomial-time strategy $\widetilde{G}$ can generate
valid output for G's i'th output block with entropy greater than k. A generator
has inaccessible entropy if the total accessible entropy (summed over the
blocks) is noticeably smaller than the real entropy of G's output.
  As an application of the above notion, we improve upon the result of Haitner,
Nguyen, Ong, Reingold, and Vadhan [Sicomp '09], presenting a much simpler and
more efficient construction of statistically hiding commitment schemes from
arbitrary one-way functions.
