A Privacy Preservation Publishing Approach in Big Data Streams

Abstract

With the recent remarkable and fast evolution in telecommunication and computing technologies, great amounts of individuals’ data are collected and used by several organizations in the society. This includes diverse data sources, often for data of high dimensionality. At the same time, most of these data are stored in tabular format and can include sensitive content.

In some cases, some organizations need to share these gathered data to be used in business analysis, decision making or scientific research purposes, which can involve sensitive information about individuals. However, these data cannot be published in their original form to other third parties, due to the associated privacy concerns. Consequently, preserving data privacy represents an essential task in order to allow such data to be published with the guarantee of preserving individuals’ privacy when sharing their included private data. This protects the identity of individuals from being discovered, and their sensitive information from being disclosed by any intruder through the published data. The data required to be published can be static or dynam