The author uses Gaussian approximation techniques and stochastic integral representation theory for counting processes to obtain a law of the iterated logarithm for kernel density estimators of a random variable $X$ observed under random censorship and truncation, and under the assumption that the density of $X$ is twice continuously differentiable.