Attack Risk Analysis in Data Anonymization in Internet of Things
Tianli Yang, Shancang Li, Muddesar Iqbal, Dhafer Jaber Almakhles · IEEE Transactions on Computational Social Systems · 2023
An enormous volume of data is generated in the Internet of Things (IoT), which needs to be anonymized before sharing with public or third parties to minimize reidentification risk and protect sensitive information. Data anonymization techniques can remove information capable of identifying individuals. However, inappropriate data anonymization can increase the risk of reidentification. This work focuses on potential attack risks of anonymized data by evaluating the potential attack risks. Specifically, we analyzed the attack risks over anonymized data with both randomization and generalization techniques. We also analyzed the risk of reidentification for five commonly used data anonymization techniques. The experimental results demonstrate that the proposed solution can well evaluate the potential attack risks.