  Widespread usage of complex interconnected social networks such as Facebook,
Twitter and LinkedIn in modern internet era has also unfortunately opened the
door for privacy violation of users of such networks by malicious entities. In
this article we investigate, both theoretically and empirically, privacy
violation measures of large networks under active attacks that was recently
introduced in (Information Sciences, 328, 403-417, 2016). Our theoretical
result indicates that the network manager responsible for prevention of privacy
violation must be very careful in designing the network if its topology does
not contain a cycle. Our empirical results shed light on privacy violation
properties of eight real social networks as well as a large number of synthetic
networks generated by both the classical Erdos-Renyi model and the scale-free
random networks generated by the Barabasi-Albert preferential-attachment model.
