  With the development of the Internet, new kinds of massive epidemics,
distributed attacks, virtual conflicts and criminality have emerged. We present
a study of some striking statistical properties of cyber-risks that quantify
the distribution and time evolution of information risks on the Internet, to
understand their mechanisms, and create opportunities to mitigate, control,
predict and insure them at a global scale. First, we report an exceptionnaly
stable power-law tail distribution of personal identity losses per event, ${\rm
Pr}({\rm ID loss} \geq V) \sim 1/V^b$, with $b =0.7 \pm 0.1$. This result is
robust against a surprising strong non-stationary growth of ID losses
culminating in July 2006 followed by a more stationary phase. Moreover, this
distribution is identical for different types and sizes of targeted
organizations. Since $b<1$, the cumulative number of all losses over all events
up to time $t$ increases faster-than-linear with time according to
$\mathbf{\simeq t^{1/b}}$, suggesting that privacy, characterized by personal
identities, is necessarily becoming more and more insecure. We also show the
existence of a size effect, such that the largest possible ID losses per event
grow faster-than-linearly as $\sim S^{1.3}$ with the organization size $S$. The
small value $b \simeq 0.7$ of the power law distribution of ID losses is
explained by the interplay between Zipf's law and the size effect. We also
infer that compromised entities exhibit basically the same probability to incur
a small or large loss.
