Faculty Publications
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Item Comments on 'Outsourcing Eigen-Decomposition and Singular Value Decomposition of Large Matrix to a Public Cloud'(Institute of Electrical and Electronics Engineers Inc., 2024) Rath, S.; Ramalingam, J.The outsourcing protocols for Eigen-Decomposition (ED) and Singular Value Decomposition (SVD) proposed by Zhou and Li (2016) offer intriguing advancements but are susceptible to malicious behavior by cloud entities. Our investigation identifies a critical vulnerability in the verification scheme utilized by Zhou and Li, where a malicious cloud can deceive the client by providing incorrect results that pass the verification step undetected. This paper not only demonstrates this vulnerability through a detailed attack scenario but also proposes an enhanced verification method to fortify the protocols against such malicious activities, ensuring the integrity and reliability of the schemes proposed by Zhou and Li. © 2013 IEEE.Item Privacy-Preserving Outsourcing Algorithm for Solving Large Systems of Linear Equations(Springer, 2023) Rath, S.; Ramalingam, J.In recent years, the secure offloading of resource-intensive computations to third-party servers has gained significant attention, thanks to the availability of computing services provided by major cloud service providers. In this paper, we propose a novel algorithm that addresses the secure outsourcing of computation for solving large-scale System of Linear Equations (SLEs). The proposed algorithm introduces a unique transformation technique to encrypt a given SLE, effectively tackling the security challenges that have been posed or raised by previous related algorithms. In contrast to prior algorithms, which focused on SLEs with a full-rank coefficient matrix, our algorithm is the first of its kind, compatible with all variations of large-scale SLEs, effectively finding a solution if one exists. Moreover, our suggested approach ensures a one-round client–cloud interaction, and allowing the client to verify the trustworthiness of the cloud server with a probability of 1. For the experimental analysis, we utilized a GPU server, specifically the Tesla V100-PCIE, as the cloud-side server. Furthermore, through a comprehensive theoretical analysis and experimental comparisons with the best-known algorithm [IEEE TIFS, 2014], we demonstrate the effectiveness of our approach. The results show that our algorithm outperforms the best-known algorithm in terms of efficiency, thereby solidifying its superiority in solving large-scale SLEs. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.Item On Efficient Parallel Secure Outsourcing of Modular Exponentiation to Cloud for IoT Applications(Multidisciplinary Digital Publishing Institute (MDPI), 2024) Rath, S.; Ramalingam, J.; Lee, C.-C.Modular exponentiation is crucial for secure data exchange in cryptography, especially for resource-constrained Internet of Things (IoT) devices. These devices often rely on third-party servers to handle computationally intensive tasks like modular exponentiation. However, existing outsourcing solutions for the RSA algorithm may have security vulnerabilities. This work identifies a critical flaw in a recent outsourcing protocol for RSA proposed by Hu et al. We demonstrate how this flaw compromises the security of the entire RSA system. Subsequently, we propose a robust solution that strengthens the RSA algorithm and mitigates the identified vulnerability. Furthermore, our solution remains resilient against existing lattice-based attacks. The proposed fix offers a more secure and efficient way for IoT devices to leverage the power of third-party servers while maintaining data integrity and confidentiality. An extensive performance evaluation confirms that our solution offers comparable efficiency while significantly enhancing security compared to existing approaches. © 2024 by the authors.Item Accelerating QKD post-processing by secure offloading of information reconciliation(Elsevier Ltd, 2024) Ramalingam, J.; Rath, S.; Kuppusamy, L.; Lee, C.-C.While quantum key distribution (QKD) offers unparalleled security in communication, its real-world application is hindered by inherent physical constraints. The challenge lies predominantly in the cumbersome, energy-intensive nature of current QKD systems, which stems largely from the time-intensive post-processing stage. This paper investigates the feasibility of offloading the computationally intensive post-processing tasks, specifically focusing on information reconciliation (IR), to potentially untrusted servers. We present a novel scheme that leverages syndrome decoding techniques to efficiently transfer the IR step of QKD protocols to a single external server. Notably, this offloading is accomplished while maintaining the highest level of security, known as unconditional security. The proposed technique is bolstered by a comprehensive theoretical analysis and validated through experimental trials. These findings demonstrate the effectiveness of our approach in bridging the gap between the theoretical promise of QKD and its real-world deployment. © 2024 Elsevier LtdItem A Note on “Secure and Efficient Outsourcing of PCA-Based Face Recognition”(Institute of Electrical and Electronics Engineers Inc., 2025) Rath, S.; Ramalingam, J.; Seal, S.Zhang et al. (2020) exhibit a fundamental mathematical flaw that renders their algorithm infeasible. Additionally, existing outsourcing protocols for PCA-based face recognition suffer from inadequate verification methods, undermining the reliability of these algorithms. © 2005-2012 IEEE.