Protecting Big Data Through Microaggregation Technique for Secured Cyber-Physical Systems

Secured cyber-physical systems (CPS) requires reliable handling of a high volume of sensitive data, which is in many cases integrated from several distributed sources. This data can usually be interconnected with physical applications, such as power grids or SCADA systems. As most of these datasets store records using numerical values, many of the microaggregation techniques are developed and tested on numerical data. These algorithms are not suitable when the data is stored as it is containing both numerical and categorical data are stored. In this chapter, the available microaggregation techniques are explored and assessed with a new microaggregation technique which can provide data anonymity regardless of its type. In this method, records are clustered into several groups using an evolutionary attribute grouping algorithm and groups are aggregated using a new operator.