To deal with the key-exposure problem in signature systems, a new framework named parallel key-insulated signature (PKIS) was introduced, and a concrete PKIS scheme was proposed. Compared with traditional key-insulated signature (KIS) schemes, the proposed PKIS scheme allows a frequent updating for temporary secret keys without increasing the risk of helper key-exposure. Moreover, the proposed PKIS scheme does not collapse even if some (not all) of the helper keys and some of the temporary secret keys are simultaneously exposed. As a result, the security of the PKIS scheme is greatly enhanced, and the damage caused by key-exposure is successfully minimized.
In this paper, we study the problem of employ ensemble learning for computer forensic. We propose a Lazy Local Learning based bagging （L3B） approach, where base learners are trained from a small instance subset surrounding each test instance. More specifically, given a test instance x, L3B first discovers x＇s k nearest neighbours, and then applies progressive sampling to the selected neighbours to train a set of base classifiers, by using a given very weak （VW） learner. At the last stage, x is labeled as the most frequently voted class of all base classifiers. Finally, we apply the proposed L3B to computer forensic.
