Kernel Metadata Declaration — on demand

What the DOI identifies

PMDP: A Secure Multiparty Computation Framework for Maintaining Multiparty Data Privacy in Cloud Computing

10.46243/jst.2022.v7.i010.pp163-174 · JournalArticle — an article in a journal · Digital · Visual · Language · en

Published 2022-12-01

Part of Journal of Science & Technology · ISSN 2456-5660 · vol. 7 · no. 10 · pp. 163–174

Principal agents

• Venkata Surya Bhavana Harish Gollavilli Venkata Surya Bhavana Harish Gollavilli (author → Author)
• Longman Publishers (publisher → Publisher)

Also in the record, outside the Kernel: the abstract, the licence, 4 links, 12 references. Source: Crossref (member 25296), registered 2024-07-03, last deposited 2026-09-17.

⬇ Record (JSON) ⬇ Declaration (XML)

System metadata — ISO 26324:2025, Annex B · DOI Handbook 10.1

Each element by the standard's name and the Handbook's (in grey), read off the record.

The record

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        "value": "Ensuring the privacy and security of sensitive information is critical in the age of cloud computing, as data sharing and collaboration grow more common. Secure Multiparty Computation (MPC) appears as a viable cryptographic solution that allows several par ties to collaborate and compute functions over their inputs while maintaining data confidentiality. To address the need for multiparty data privacy protection in cloud computing scenarios, the Privacy -preserving Multiparty Data Privacy (PMDP) framework is introduced. PMDP uses advanced cryptography methods and privacy -preserving mechanisms to protect sensitive data from semi -malicious adversaries. The framework takes advantage of the NTRU encryption scheme's ring structure, employing polynomial -based key ge neration, encryption, and decryption algorithms using a public-private key pair. PMDP also uses the Sample -and-Aggregate algorithm to segment, clip, and aggregate datasets for calculations, as well as Laplace noise to improve security. Furthermore, PMDP incorporates differential privacy concepts to formalize privacy guarantees by restricting the influence of individual data on query results. PMDP was developed collaboratively, drawing on experience from a variety of disciplines such as cloud computing, encr yption, and privacy - preserving technologies. Thorough integration and testing processes verify the framework's functionality, durability, and efficacy in real -world cloud computing scenarios. PMDP's performance is evaluated against existing cryptographic a pproaches, and user feedback and iterative improvement are used to continuously improve the framework's usability and effectiveness. Overall, the systematic methodology used in the design, implementation, and evaluation of PMDP emphasizes its importance as a solid solution for protecting multiparty data privacy",
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            "unstructured": "Laud, P., & Pankova, A. (2018). Privacy-preserving record linkage in large databases using secure multiparty computation. BMC medical genomics, 11, 33-46"
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            "doi": "10.2196/12702",
            "unstructured": "Dankar, F. K., Madathil, N., Dankar, S. K., & Boughorbel, S. (2019). Privacy-preserving analysis of distributed biomedical data: designing efficient and secure multiparty computations using distributed statistical learning theory. JMIR medical informatics, 7(2), e12702"
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            "doi": "10.1186/s12911-016-0316-1",
            "unstructured": "Shi, H., Jiang, C., Dai, W., Jiang, X., Tang, Y., Ohno-Machado, L., & Wang, S. (2016). Secure multi-pArty computation grid LOgistic REgression (SMAC-GLORE). BMC medical informatics and decision making, 16, 175-187"
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            "key": "ref4",
            "doi": "10.1038/nbt.4108",
            "unstructured": "Cho, H., Wu, D. J., & Berger, B. (2018). Secure genome-wide association analysis using multiparty computation. Nature biotechnology, 36(6), 547-551"
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            "doi": "10.1109/secdev.2016.028",
            "unstructured": "Hogan, K., Luther, N., Schear, N., Shen, E., Stott, D., Yakoubov, S., & Yerukhimovich, A. (2016, November). Secure multiparty computation for cooperative cyber risk assessment. In 2016 IEEE Cybersecurity Development (SecDev) (pp. 75-76). IEEE"
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            "doi": "10.1145/2818000.2818027",
            "unstructured": "Pettai, M., & Laud, P. (2015, December). Combining differential privacy and secure multiparty computation. In Proceedings of the 31st annual computer security applications conference (pp. 421-430)"
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            "doi": "10.1016/j.future.2016.10.022",
            "unstructured": "Nayahi, J. J. V., & Kavitha, V. (2017). Privacy and utility preserving data clustering for data anonymization and distribution on Hadoop. Future Generation Computer Systems, 74, 393-408"
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            "unstructured": "Aldeen, Y. A. A. S., Salleh, M., & Aljeroudi, Y. (2016). An innovative privacy preserving technique for incremental datasets on cloud computing. Journal of biomedical informatics, 62, 107-116"
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            "unstructured": "Granados Moreno, P., Joly, Y., & Knoppers, B. M. (2017). Public–private partnerships in cloud-computing services in the context of genomic research. Frontiers in medicine, 4, 3"
        },
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            "key": "ref11",
            "doi": "10.3390/s16020179",
            "unstructured": "Zhu, H., Gao, L., & Li, H. (2016). Secure and privacy-preserving body sensor data collection and query scheme. Sensors, 16(2), 179"
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            "doi": "10.3390/s18072158",
            "unstructured": "Wu, A., Zheng, D., Zhang, Y., & Yang, M. (2018). Hidden policy attribute-based data sharing with direct revocation and keyword search in cloud computing. Sensors, 18(7), 2158"
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The Kernel Metadata Declaration

DOIMetadataKernel.xsd · namespace http://www.doi.org/2010/DOISchema · Attribute Value Sets 2.3 · draft — the agency's DOI name is filled in on accreditation

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