| Full text | |
| Author(s): |
Coelho, Kristtopher K.
;
Okuyama, Mauricio M.
;
Nogueira, Michele
;
Vieira, Alex B.
;
Silva, Edelberto E.
;
Miranda Nacif, Jose Augusto
Total Authors: 6
|
| Document type: | Journal article |
| Source: | 2024 IEEE SYMPOSIUM ON COMPUTERS AND COMMUNICATIONS, ISCC 2024; v. N/A, p. 6-pg., 2024-01-01. |
| Abstract | |
Technological advances enable the integration of Internet of Things (IoT) devices to perform continuous and proactive patient monitoring. These devices collect a large volume of sensitive data that requires privacy. Anonymization provides privacy by removing or modifying information that identifies an individual. However, traditional anonymization techniques, such as k-anonymity, depend on a fixed and pre-defined k value, susceptible to attribute disclosure attacks. This article presents Dynamic Anonymization by Separatrices (DAS), an approach for defining the ideal value k and for dynamic grouping of data to be anonymized using separatrices measurements. Results show that the proposed approach efficiently mitigates attribute disclosure attacks. (AU) | |
| FAPESP's process: | 18/23098-0 - MENTORED: from modeling to experimentation - predicting and detecting DDoS and zero-day attacks |
| Grantee: | Michele Nogueira Lima |
| Support Opportunities: | Research Projects - Thematic Grants |