Blockchain‐Based Privacy Protection Scheme for IoT‐Assisted Educational Big Data Management
Author(s) -
Xiaoshuang He,
Hechuan Guo,
Xueyu Cheng
Publication year - 2021
Publication title -
wireless communications and mobile computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.42
H-Index - 64
eISSN - 1530-8677
pISSN - 1530-8669
DOI - 10.1155/2021/3558972
Subject(s) - blockchain , computer science , computer security , scheme (mathematics) , internet of things , privacy protection , internet privacy , big data , data management , database , data mining , mathematical analysis , mathematics
Adoption of the Internet of Things (IoT) in education brings many benefits. However, the poor implementation of access control of educational data produced by the IoT devices has brought students’ and teachers’ privacy into danger. Attackers can access educational data that they are not permitted to access and even erase the records during access. To tackle this problem, we employ blockchain technology to guarantee the integrity of access control rules and trace the records of access events. In this paper, we propose a blockchain-based access control scheme for the data produced by IoT devices. The scheme consists of three components: (1) a well-implemented data collection module that is deployed in smart classrooms, which collects and uploads data about the real-time situation inside the smart classroom to the data center; (2) a MongoDB-based data center and its control module that makes access control decisions based on the verification of the permissions of visitors, where the permissions are managed by blockchain; and (3) a customized blockchain system that stores and keeps security policy updates of the role-based access control module and records access events in a trusted way. Our analysis indicates that the proposed access control scheme guarantees the correctness of the access control process and makes the access of collected educational data auditable and responsible. Our system collectively analyzes the context of the smart classroom and is capable of detecting multiple scenarios such as absence, lateness, and gunshot. We show how the scheme preserves students’ and teachers’ privacy by carrying out extensive experimental studies. The results indicate that the proposed data management system can give correct responses as quickly as a traditional data server does while preserving privacy.
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