An Efficient Secure Coded Edge Computing Scheme Using Orthogonal Vector
Title | An Efficient Secure Coded Edge Computing Scheme Using Orthogonal Vector |
Publication Type | Conference Paper |
Year of Publication | 2019 |
Authors | Zhou, Wei, Wang, Jin, Li, Lingzhi, Wang, Jianping, Lu, Kejie, Zhou, Xiaobo |
Conference Name | 2019 IEEE Intl Conf on Parallel Distributed Processing with Applications, Big Data Cloud Computing, Sustainable Computing Communications, Social Computing Networking (ISPA/BDCloud/SocialCom/SustainCom) |
Date Published | dec |
ISBN Number | 978-1-7281-4328-6 |
Keywords | actual EC deployments, cloud computing, coding theory, communication overhead, computational complexity, compute-intensive applications, data confidentiality, data matrix, Data security, Decoding, edge computing, edge devices, efficient edge, efficient secure coded edge computing scheme, high decoding complexities, Human Behavior, human factors, information theoretic security, information theoretical security, irrelevant random blocks, last mile problem, linear codes, linear coding, Metrics, original data blocks, orthogonal vector, policy-based governance, pubcrawl, resilience, Resiliency, Scalability, secure edge, security, security of data, unreliable edge nodes, user device, Vectors, widespread solution |
Abstract | In recent years, Edge Computing (EC) has attracted increasing attention for its advantages in handling latencysensitive and compute-intensive applications. It is becoming a widespread solution to solve the last mile problem of cloud computing. However, in actual EC deployments, data confidentiality becomes an unignorable issue because edge devices may be untrusted. In this paper, a secure and efficient edge computing scheme based on linear coding is proposed. Generally, linear coding can be utilized to achieve data confidentiality by encoding random blocks with original data blocks before they are distributed to unreliable edge nodes. However, the addition of a large amount of irrelevant random blocks also brings great communication overhead and high decoding complexities. In this paper, we focus on the design of secure coded edge computing using orthogonal vector to protect the information theoretic security of the data matrix stored on edge nodes and the input matrix uploaded by the user device, while to further reduce the communication overhead and decoding complexities. In recent years, Edge Computing (EC) has attracted increasing attention for its advantages in handling latencysensitive and compute-intensive applications. It is becoming a widespread solution to solve the last mile problem of cloud computing. However, in actual EC deployments, data confidentiality becomes an unignorable issue because edge devices may be untrusted. In this paper, a secure and efficient edge computing scheme based on linear coding is proposed. Generally, linear coding can be utilized to achieve data confidentiality by encoding random blocks with original data blocks before they are distributed to unreliable edge nodes. However, the addition of a large amount of irrelevant random blocks also brings great communication overhead and high decoding complexities. In this paper, we focus on the design of secure coded edge computing using orthogonal vector to protect the information theoretic security of the data matrix stored on edge nodes and the input matrix uploaded by the user device, while to further reduce the communication overhead and decoding complexities. |
URL | https://ieeexplore.ieee.org/document/9047418 |
DOI | 10.1109/ISPA-BDCloud-SustainCom-SocialCom48970.2019.00025 |
Citation Key | zhou_efficient_2019 |
- resilience
- irrelevant random blocks
- last mile problem
- linear codes
- linear coding
- Metrics
- original data blocks
- orthogonal vector
- policy-based governance
- pubcrawl
- information theoretical security
- Resiliency
- Scalability
- secure edge
- security of data
- unreliable edge nodes
- user device
- Vectors
- widespread solution
- Decoding
- security
- actual EC deployments
- Cloud Computing
- communication overhead
- computational complexity
- compute-intensive applications
- data confidentiality
- data matrix
- Data Security
- coding theory
- edge computing
- edge devices
- efficient edge
- efficient secure coded edge computing scheme
- high decoding complexities
- Human behavior
- Human Factors
- information theoretic security