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CASE: A Context-Aware Security Scheme for Preserving Data Privacy in IoT-Enabled Society 5.0

This article introduces the concept of context-aware attribute learning with cipher policy-attribute-based encryption (CP-ABE) to preserve the privacy of users’ information in IoT-enabled Society 5.0. The concept of Society 5.0 pioneers an abstract system unifying different smart environments (SEs) to provide seamless services to the citizens. While serving different applications, these SEs store users’ information in the cloud engendering users’ privacy. CP-ABE is one of the conventional security systems that preserves privacy with group data accessibility. Contemporary CP-ABE solutions enforce users to manually provide their contextual information, namely, attributes, to encrypt/decrypt data. From these solutions it can be conjectured that incorrect attribute selection by a user raises the issue of unauthenticated access to information. To address these issues, we propose a scheme, named the context-aware attribute learning scheme (CASE), which autonomously learns users’ contextual information, exploiting edge intelligence, generates attributes, and reduces the post-encryption data size using the learned attributes. We examine the performance of CASE with the help of a case study on CP-ABE over smart healthcare systems (SHSs). Extensive experimental results show that CASE outperforms the existing CP-ABE-based security schemes by reducing 32%–33% average network delay, 33%–35% average energy consumption, and 31%–36% average packet loss. Additionally, we analyze the performance of attribute learning schemes using the support vector machine (SVM), decision tree (DT), and naive Bayes (NB) learning models. We observe that DT reports better performance over SVM and NB in prediction accuracy, prediction time, and clock cycles required for execution.