Let $X$ be a positive random variable with continuous distribution function $F$, quantile function $Q$ and finite mean $\mu>0$. The Lorenz curve, corresponding to the random variable $X$, is defined by $$ L(u)={1\over\mu}\int_0^u Q(y)\, du. $$ The concentration curve is defined as $L^{-1}$, the inverse of the Lorenz curve. In the paper under review their empirical estimators are considered. Under certain assumptions strong Gaussian approximations are shown for empirical Lorenz and concentration processes in the case of truncated and censored data. As consequences, functional laws of the iterated logarithm are established.