Biblio

Cyber breaches can result in disruption to business operations, reputation damage as well as directly affecting the financial stability of the targeted corporations, with potential impacts on future profits and stock values. Automatic network-stream monitoring becomes necessary for cyber situation awareness, and time-series anomaly detection plays an important role in network stream monitoring. This study surveyed recent research on time-series analysis methods in respect of parametric and non-parametric techniques, and popular machine learning platforms for data analysis on streaming data on both single server and cloud computing environments. We believe it provides a good reference for researchers in both academia and industry to select suitable (time series) data analysis techniques, and computing platforms, dependent on the data scale and real-time requirements.

Generating synthetic data is a well-known option to limit disclosure risk in sensitive data releases. The usual approach is to build a model for the population and then generate a synthetic data set solely based on the model. We argue that building an accurate population model is difficult and we propose instead to approximate the original data as closely as privacy constraints permit. To enforce an ex ante privacy level when generating synthetic data, we introduce a new privacy model called $ε$ synthetic privacy. Then, we describe a synthetic data generation method that satisfies $ε$-synthetic privacy. Finally, we evaluate the utility of the synthetic data generated with our method.