One of the biggest concerns of big data is privacy. However, the study on big data privacy is
still at a very early stage. We believe the forthcoming solutions and theories of big data …

Given two random variables X and Y, an operational approach is undertaken to quantify the
“leakage” of information from X to Y. The resulting measure L (X→ Y) is called maximal …

Anonymous communication has become a hot research topic in order to meet the increasing
demand for web privacy protection. However, there are few such systems which can provide …

t-Closeness is a privacy model recently defined for data anonymization. A data set is said to
satisfy t-closeness if, for each group of records sharing a combination of key attributes, the …

Given two discrete random variables X and Y, an operational approach is undertaken to
quantify the “leakage” of information from X to Y. The resulting measure ℒ (X→ Y) is called …

Distributed Denial-of-Service (DDoS) attacks are usually launched through the botnet, an
“army” of compromised nodes hidden in the network. Inferential tools for DDoS mitigation …

In a Distributed Denial of Service (DDoS) attack, a network (botnet) of dispersed agents
(bots) sends requests to a website to saturate its resources. Since the requests are sent by …

This work examines propagation of cyber-threats over networks under an adversarial
formulation. Exploiting Kendall's birth-death-immigration model, we propose an analytical …

Low latency anonymity systems are susceptive to traffic analysis attacks. In this paper, we
propose a dependent link padding scheme to protect anonymity systems from traffic analysis …

Research has proved that supposedly secure encrypted network traffic is actually threatened
by privacy and security violations from many aspects. This is mainly due to flow features …