| Title | Trajectory-observers of timed stochastic discrete event systems: Applications to privacy analysis |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Lefebvre, Dimitri, Hadjicostis, Christoforos N. |
| Conference Name | 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT) |
| Keywords | data privacy, Discrete event system, discrete event systems, k-step trajectory-observer, language-based opacity, Markov model, Metrics, Observers, Petri nets, privacy, privacy analysis, privacy models and measurement, pubcrawl, security, security of data, stochastic petri nets, Stochastic processes, timed stochastic discrete event systems, timed stochastic Petri net models |
| Abstract | Various aspects of security and privacy in many application domains can be assessed based on proper analysis of successive measurements that are collected on a given system. This work is devoted to such issues in the context of timed stochastic Petri net models. We assume that certain events and part of the marking trajectories are observable to adversaries who aim to determine when the system is performing secret operations, such as time intervals during which the system is executing certain critical sequences of events (as captured, for instance, in language-based opacity formulations). The combined use of the k-step trajectory-observer and the Markov model of the stochastic Petri net leads to probabilistic indicators helpful for evaluating language-based opacity of the given system, related timing aspects, and possible strategies to improve them. |
| DOI | 10.1109/CoDIT.2019.8820669 |
| Citation Key | lefebvre_trajectory-observers_2019 |
Trajectory-observers of timed stochastic discrete event systems: Applications to privacy analysis