Edge Computing Assisted an Efficient Privacy Protection Layered Data Aggregation Scheme for IIoT

Abstract

The emergence of edge computing has improved the real time and efficiency of the Industrial Internet of Things. In order to achieve safe and efficient data collection and application in the Industrial Internet of Things, a lot of computing and bandwidth resources are usually sacrificed. From the perspective of low computing and communication overhead, this paper proposes an efficient privacy protection layered data aggregation scheme for edge computing assisted IIoT by combining the Chinese Remainder Theorem (CRT), improved Paillier homomorphic algorithm, and hash chain technology (edge computing assisted an efficient privacy protection layered data aggregation scheme for IIoT, EE-PPDA). In EE-PPDA, first, a layered aggregation architecture based on edge computing is designed. Edge nodes and cloud are responsible for local aggregation and global aggregation, respectively, which effectively reduces the amount of data transmission. At the same time, EE-PPDA achieves data confidentiality through improved Paillier encryption, ensuring that neither attackers nor semitrusted nodes (e.g., edge nodes and clouds) can know the private data of a single device, and it can resist by simply using hash chains to resist tampering and pollution attacks ensure data integrity. Second, according to the CRT, the cloud can obtain the fine-grained aggregation results of subregions from the global aggregation results, thereby providing fine-grained data services. In addition, the EE-PPDA scheme also supports fault tolerance. Even if some IIoT devices or communication links fail, the cloud can still decrypt incomplete aggregated ciphertexts and obtain the expected aggregation results. Finally, the performance evaluation shows that the proposed EE-PPDA scheme has less calculation and communication costs.

Combining Machine Learning and Edge Computing: Opportunities, Challenges, Platforms, Frameworks, and Use Cases

“…Anonymization and pseudonymization techniques are utilized to remove or replace personally identifiable information (PII) from data sets, preventing the identification of individuals while allowing for data analysis [21,22]. Data minimization strategies such as aggregation and summarization help reduce the amount of data processed at the edge, minimizing privacy risks [16,23]. Privacy-preserving machine learning techniques such as federated learning [24,25] and secure model aggregation [26] allow for training machine learning models on distributed edge devices without exposing raw data.…”

Transforming data‐intensive workflows: A cutting‐edge multi‐layer security and quality aware security framework

“…The Industrial Internet of Things provides a secure and risk-free environment for data collection and analysis. 6 It was possible to demonstrate a layered data aggregation system that protects users' privacy and is enabled by edge computing for the Internet of Industrial Things using the Chinese Remainder Theorem (CRT), an upgraded paillier homomorphic algorithm, and hash chain technology. This technique guards against tampering and pollution attacks by utilizing improved paillier encryption and hash chains, preserving data integrity and allowing it to accept flaws.…”

“…As a result, there is a greater risk of intermediate data being targeted via node and link attacks; as a result, edge-cloud platform availability [3][4][5] must be guaranteed. However, providing security to workflow execution in an edge-cloud platform is difficult, 6 requiring QoS such as reduced time and energy consumption and significant dependencies between sub-tasks. 7 The Multi-Layer Security and Quality Aware (MLSQA) workflow is intended to address research issues 8 by developing a solid and efficient approach for carrying out extensive data processes.…”

“…As a result, there is a greater risk of intermediate data being targeted via node and link attacks; as a result, edge‐cloud platform availability 3–5 must be guaranteed. However, providing security to workflow execution in an edge‐cloud platform is difficult, 6 requiring QoS such as reduced time and energy consumption and significant dependencies between sub‐tasks 7 …”