Biblio

With the construction and implementation of the government information resources sharing mechanism, the protection of citizens' privacy has become a vital issue for government departments and the public. This paper discusses the risk of citizens' privacy disclosure related to data sharing among government departments, and analyzes the current major privacy protection models for data sharing. Aiming at the issues of low efficiency and low reliability in existing e-government applications, a statistical data sharing framework among governmental departments based on local differential privacy and blockchain is established, and its applicability and advantages are illustrated through example analysis. The characteristics of the private blockchain enhance the security, credibility and responsiveness of information sharing between departments. Local differential privacy provides better usability and security for sharing statistics. It not only keeps statistics available, but also protects the privacy of citizens.

Hidden Markov Models (HMM) are probabilistic models that can be used for forecasting time series data. It has seen success in various domains like finance [1-5], bioinformatics [6-8], healthcare [9-11], agriculture [12-14], artificial intelligence[15-17]. However, the use of HMM in cyber security found to date is numbered. We believe the properties of HMM being predictive, probabilistic, and its ability to model different naturally occurring states form a good basis to model cyber security data. It is hence the motivation of this work to provide the initial results of our attempts to predict security attacks using HMM. A large network datasets representing cyber security attacks have been used in this work to establish an expert system. The characteristics of attacker's IP addresses can be extracted from our integrated datasets to generate statistical data. The cyber security expert provides the weight of each attribute and forms a scoring system by annotating the log history. We applied HMM to distinguish between a cyber security attack, unsure and no attack by first breaking the data into 3 cluster using Fuzzy K mean (FKM), then manually label a small data (Analyst Intuition) and finally use HMM state-based approach. By doing so, our results are very encouraging as compare to finding anomaly in a cyber security log, which generally results in creating huge amount of false detection.