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(L^3B) approach,where base earners are trained from a small instance subset surrounding each test instance.More specifically,given a test instance x,L^3B 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 L^3B to computer forensic.
