Article
Computer Science, Information Systems
Wei Du, Ang Li, Qinghua Li, Pan Zhou
Summary: The increasing volume of data presents a challenge for users with limited resources to process and analyze it. One solution is to outsource computation-intensive tasks to the cloud for its powerful computing capability. However, this raises privacy and security concerns. This article focuses on the privacy-preserving and secure outsourcing of large-scale nonlinear programming problems in the context of cloud computing and proposes an outsourcing protocol that encrypts private information and uses efficient methods to solve the transformed problems.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2023)
Article
Computer Science, Information Systems
Xiulan Li, Jingguo Bi, Chengliang Tian, Hanlin Zhang, Jia Yu, Yanbin Pan
Summary: This article highlights the importance of solving quadratic congruence equations in secure Internet of Things applications. Two passive attacks are presented to demonstrate the insecurity of previous outsourcing algorithms. To address this issue, the authors propose an improved outsourcing algorithm that is efficient and protects the privacy of actual inputs and outputs.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Cybernetics
Hanlin Zhang, Jia Yu, Mohammad S. Obaidat, Pandi Vijayakumar, Linqiang Ge, Jie Lin, Jianxi Fan, Rong Hao
Summary: This article addresses the issue of resource-constrained IoT devices being unable to perform complex computations. A secure edge-aided computation scheme is proposed, which includes a framework, identification of security threats, and definition of security requirements. Two secure outsourcing algorithms meeting the requirements (matrix multiplication and modular exponentiation) are provided, with efficiency and security supported by theoretical analysis and experimental results.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2022)
Article
Computer Science, Information Systems
Hongjun Li, Jia Yu, Ming Yang, Fanyu Kong
Summary: In this article, an efficient and secure outsourcing algorithm is proposed for solving large-scale convex optimization problems in the Internet of Things (IoT). The algorithm reduces computational complexity on the client side, protects sensitive data, and enables detection of malicious behavior from the cloud server.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Information Systems
Qilin Hu, Mingxing Duan, Zhibang Yang, Siyang Yu, Bin Xiao
Summary: This article introduces a parallel secure outsourcing scheme for modular exponentiation operation in IoT devices with resource constraints, providing enhanced security and efficiency. Experimental results indicate the superiority of the scheme in scalability and time consumption over previous methods.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Information Systems
Yan He, Liang Feng Zhang
Summary: This study proposes a multi-matrix verifiable computation scheme that allows secure outsourcing of matrix function computation. Compared to the previous best known scheme, it is roughly m times faster and has lower communication cost in both client-side and server-side computation.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Hongjun Li, Fanyu Kong, Jia Yu
Summary: With the rise of cloud computing, outsourcing computation has become a popular service in academic and industry sectors. In this article, a secure and efficient algorithm is proposed to outsource spectral decomposition to an untrusted cloud server, protecting both input and output privacy while ensuring correctness through efficient verification. The results not only reduce computational overhead for clients, but also do not add extra workload on the cloud server, with theoretical analysis and experimental results provided.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Information Systems
Jialin Li, Penghao Lu, Xuemin Lin
Summary: In this paper, a novel multifunctional and privacy-preserving outsourcing computation toolkit is proposed, which supports complex computing tasks such as machine learning and provides efficient and feasible homomorphic conversion protocols and homomorphic K-means algorithm.
Article
Computer Science, Information Systems
Wenjing Gao, Jia Yu
Summary: This paper introduces a parallel outsourcing mechanism based on two edge servers to accelerate the computation of matrix determinant. The computation task is divided into multiple subtasks using the matrix blocking technique, which are then assigned to the edge servers for parallel computation. Additionally, a privacy-preserving matrix transformation technique is proposed to protect data privacy. The correctness, privacy, and verifiability of the protocol are analyzed, and the performance advantage is demonstrated through simulation experiments.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2023)
Article
Computer Science, Hardware & Architecture
Shlomi Dolev, Peeyush Gupta, Yin Li, Sharad Mehrotra, Shantanu Sharma
Summary: The paper introduces algorithms for data outsourcing based on Shamir's secret-sharing scheme and for executing privacy-preserving SQL queries using MapReduce. These algorithms prevent an adversary from knowing the database or the query, and also prevent output-size and access-pattern attacks.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2021)
Article
Computer Science, Hardware & Architecture
Jin Li, Heng Ye, Tong Li, Wei Wang, Wenjing Lou, Y. Thomas Hou, Jiqiang Liu, Rongxing Lu
Summary: This article discusses the application of differential privacy in data privacy protection and proposes two schemes for outsourcing differential privacy. These schemes effectively address the issues of current differential privacy techniques in adapting to different tasks and budgets, and their effectiveness is verified through experiments.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2022)
Article
Computer Science, Information Systems
Hanlin Zhang, Le Tong, Jia Yu, Jie Lin
Summary: Bilinear pairing is a fundamental operation in cryptographic algorithms, but the existing secure outsourcing algorithms for bilinear pairing may not be suitable for resource-constrained IoT devices due to storage burden. The proposed algorithm in this article does not require precomputations, significantly improves efficiency, ensures privacy, and utilizes the Ethereum blockchain for fair payments. Theoretical analysis and experimental results demonstrate the efficiency and security of the proposed algorithm.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Information Systems
Wei-Zhe Zhang, Ibrahim A. Elgendy, Mohamed Hammad, Abdullah M. Iliyasu, Xiaojiang Du, Mohsen Guizani, Ahmed A. Abd El-Latif
Summary: Mobile-edge computing (MEC) introduces a new load balancing and computation offloading (CO) technique, along with a new security layer to address potential security issues. Experimental results demonstrate that redistributing MDUs among different sBSs can effectively reduce system consumption, while adopting AES encryption technology can better protect data during transmission.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Information Systems
Yinghui Zhang, Robert H. Deng, Ximeng Liu, Dong Zheng
Summary: Cloud computing is a milestone in the development of outsourcing services, but trust issues between users and providers may impede its wide adoption. BPay, a blockchain-based outsourcing service fair payment framework, ensures secure and fair payment without relying on any third party.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Xiaofeng Chen, Hui Li, Jin Li, Qian Wang, Xinyi Huang, Willy Susilo, Yang Xiang
Summary: This study presents a new verifiable database (VDB) scheme that supports all updating operations and introduces a new primitive tool called Committed Invertible Bloom Filter (CIBF).
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)