Volume 23, Issue 4 (2012), pp. 537–562
Hwang et al. proposed an ElGamal-like scheme for encrypting large messages, which is more efficient than its predecessor in terms of computational complexity and the amount of data transformation. They declared that the resulting scheme is semantically secure against chosen-plaintext attacks under the assumptions that the decision Diffie–Hellman problem is intractable. Later, Wang et al. pointed out that the security level of Hwang et al.'s ElGamal-like scheme is not equivalent to the original ElGamal scheme and brings about the disadvantage of possible unsuccessful decryption. At the same time, they proposed an improvement on Hwang et al.'s ElGamal-like scheme to repair the weakness and reduce the probability of unsuccessful decryption. However, in this paper, we show that their improved scheme is still insecure against chosen-plaintext attacks whether the system is operated in the quadratic residue modulus or not. Furthermore, we propose a new ElGamal-like scheme to withstand the adaptive chosen-ciphertext attacks. The security of the proposed scheme is based solely on the decision Diffie–Hellman problem in the random oracle model.
Volume 19, Issue 1 (2008), pp. 135–156
Data stream mining has become a novel research topic of growing interest in knowledge discovery. Most proposed algorithms for data stream mining assume that each data block is basically a random sample from a stationary distribution, but many databases available violate this assumption. That is, the class of an instance may change over time, known as concept drift. In this paper, we propose a Sensitive Concept Drift Probing Decision Tree algorithm (SCRIPT), which is based on the statistical X2 test, to handle the concept drift problem on data streams. Compared with the proposed methods, the advantages of SCRIPT include: a) it can avoid unnecessary system cost for stable data streams; b) it can immediately and efficiently corrects original classifier while data streams are instable; c) it is more suitable to the applications in which a sensitive detection of concept drift is required.