Open AccessThe Role of Information Theory in the Field of Big Data Privacy
Author(s) -
Mariam Haroutunian,
AUTHOR_ID,
Karen Mastoyan,
AUTHOR_ID
Publication year - 2021
Publication title -
mathematical problems of computer science
Language(s) - English
Resource type - Journals
eISSN - 2738-2788
pISSN - 2579-2784
DOI - 10.51408/1963-0071
Subject(s) - anonymity , computer science , realm , information privacy , privacy by design , differential privacy , personally identifiable information , field (mathematics) , big data , k anonymity , privacy software , internet privacy , computer security , state (computer science) , data science , data mining , political science , law , mathematics , pure mathematics , algorithm
Protecting privacy in Big Data is a rapidly growing research area. The first approach towards privacy assurance was the anonymity method. However, recent research indicated that simply anonymized data sets can be easily attacked. Later, differential privacy was proposed, which proved to be the most promising approach. The trade-off between privacy and the usefulness of published data, as well as other problems, such as the availability of metrics to compare different ways of achieving anonymity, are in the realm of Information Theory. Although a number of review articles are available in literature, the information - theoretic methods capacities haven’t been paid due attention. In the current article an overview of state-of-the-art methods from Information Theory to ensure privacy are provided.