Within the framework of UC （universally composable） security, a general method is presented to construct a secure channel protocol with using IND-CVA （indistinguishability of encryption scheme under ciphertext verification attacks）. A channel protocol with using the method first invokes an ideal keyexchange protocol to get a session key, and then computes the messages with an authenticated encryption scheme. The paper shows that a channel protocol is UC secure if and only if the underlying authenticated encryption scheme is both IND-CVA secure and INT-PTXT secure. The condition about secure channel protocol in this paper is much weaker than IND-CCA secure and INT-CTXT secure. The IND-CVA can be presented for describing the privacy requirements of secure channels in detail. Moreover, the method for designing secure channel protocol in the paper reduces the UC security of secure channels, which are measured by action-simulation in the UC security framework, to the security of authenticated encryption schemes, which are measured semantically.
This paper formally discusses the security problem caused by the ciphertext verification, presenting a new security notion named IND-CVA （indistinguishability under ciphertext verification attacks） to characterize the privacy of encryption schemes in this situation. Allowing the adversary to access to both encryption oracle and ciphertext verification oracle, the new notion IND-CVA is slightly stronger than IND-CPA （indistinguishability under chosen-plaintext attacks） but much weaker than IND-CCA （indistin- guishability under chosen-ciphertext attacks）, and can be satisfied by most of the popular symmetric encryption schemes such as OTP （one-time-pad）, CBC （cipher block chaining） and CTR （counter）. An MAC （message authentication scheme） is usually combined with an encryption to guarantee secure communication （e.g. SSH, SSL and IPSec）. However, with the notion of IND-CVA, this paper shows that a secure MAC can spoil the privacy in some cases.
Designing efficient estimation of distribution algorithms for optimizing complex continuous problems is still a challenging task. This paper utilizes histogram probabilistic model to describe the distribution of population and to generate promising solutions. The advantage of histogram model, its intrinsic multimodality, makes it proper to describe the solution distribution of complex and multimodal continuous problems. To make histogram model more efficiently explore and exploit the search space, several strategies are brought into the algorithms： the surrounding effect reduces the population size in estimating the model with a certain number of the bins and the shrinking strategy guarantees the accuracy of optimal solutions. Furthermore, this paper shows that histogram-based EDA （Estimation of distribution algorithm） can give comparable or even much better performance than those predominant EDAs based on Gaussian models.
