Affiliations: [a] Centro de Informática, Universidade Federal de Pernambuco, Pernambuco, Brasil | [b] Faculdade de Ciências da Administração de Pernambuco, Universidade de Pernambuco, Pernambuco, Brasil | [c] Universidade Federal do Ceará – Campus Quixadá, Ceará, Brasil
Abstract: The Internet of Things (IoT) has shown rapid growth in recent years. However, it presents challenges related to the lack of standardization of communication produced by different types of devices. Another problem area is the security and privacy of data generated by IoT devices. Thus, with the focus on grouping, analyzing, and classifying existing data security and privacy methods in IoT, based on data anonymization, we have conducted a Systematic Literature Review (SLR). We have therefore reviewed the history of works developing solutions for security and privacy in the IoT, particularly data anonymization and the leading technologies used by researchers in their work. We also discussed the challenges and future directions for research. The objective of the work is to give order to the main approaches that promise to provide or facilitate data privacy using anonymization in the IoT area. The study’s results can help us understand the best anonymization techniques to provide data security and privacy in IoT environments. In addition, the findings can also help us understand the limitations of existing approaches and identify areas for improvement. The results found in most of the studies analyzed indicate a lack of consensus in the following areas: (i) with regard to a solution with a standardized methodology to be applied in all scenarios that encompass IoT; (ii) the use of different techniques to anonymize the data; and (iii), the resolution of privacy issues. On the other hand, results made available by the k-anonymity technique proved efficient in combination with other techniques. In this context, data privacy presents one of the main challenges for broadening secure domains in applying privacy with anonymity.
Keywords: Internet of Things, privacy, data anonymization, k-anonymity, data flow
