Biblio

Differential privacy is a widely-used metric, which provides rigorous privacy definitions and strong privacy guarantees. Much of the existing studies on differential privacy are based on datasets where the tuples are independent, and thus are not suitable for correlated data protection. In this paper, we focus on correlated differential privacy, by taking the data correlations and the prior knowledge of the initial data into account. The data correlations are modeled by Bayesian conditional probabilities, and the prior knowledge refers to the exact values of the data. We propose general correlated differential privacy conditions for the discrete and continuous random noise-adding mechanisms, respectively. In case that the conditions are inaccurate due to the insufficient prior knowledge, we introduce the tuple dependence based on rough set theory to improve the correlated differential privacy conditions. The obtained theoretical results reveal the relationship between the correlations and the privacy parameters. Moreover, the improved privacy condition helps strengthen the mechanism utility. Finally, evaluations are conducted over a micro-grid system to verify the privacy protection levels and utility guaranteed by correlated differential private mechanisms.

In modern cyber-physical systems and wireless sensor networks the complexity of crisis management processes is caused by a variety of software/hardware assets and communication protocols, the necessity of their collaborative function, possible inconsistency of data flows between particular devices and increased requirements to cyber-physical security. A crisis management oriented model of a communicational mobile network is constructed. A general architecture of network nodes by the use of XBee circuits, Arduino microcontrollers and connecting equipment are developed. An analysis of possible cyber-physical security events on the base of existing intruder models is performed. A series of experiments on modeling attacks on network nodes is conducted. Possible ways for attack revelations by means of components for security event collection and data correlation is discussed.