A Class of Models for Uncorrelated Random Variables

We consider the class of multivariate distributions that gives the distribution of the sum of uncorrelated random variables by the product of their marginal distributions. This class is defined by a representation of the assumption of sub-independence, formulated previously in terms of the characteristic function and convolution, as a weaker assumption than independence for derivation of the distribution of the sum of random variables. The new representation is in terms of stochastic equivalence and the class of distributions is referred to as the summable uncorrelated marginals (SUM) distributions. The SUM distributions can be used as models for the joint distribution of uncorrelated random variables, irrespective of the strength of dependence between them. We provide a method for the construction of bivariate SUM distributions through linking any pair of identical symmetric probability density functions. We also give a formula for measuring the strength of dependence of the SUM models. A final result shows that under the condition of positive or negative orthant dependence, the SUM property implies independence

From Plaintext-extractability to IND-CCA Security

We say a public-key encryption is plaintext-extractable in the random oracle model if there exists an algorithm that given access to all inputs/outputs queries to the random oracles can simulate the decryption oracle. We argue that plaintext-extractability is enough to show the indistinguishably under chosen ciphertext attack (IND-CCA) of OAEP+ transform (Shoup, Crypto 2001) when the underlying trapdoor permutation is one-way. We extend the result to the quantum random oracle model (QROM) and show that OAEP+ is IND-CCA secure in QROM if the underlying trapdoor permutation is quantum one-way