SPAD – A Secure and Privacy-Preserving Distributed Analytics Framework

Makhdoom, I; Abolhasan, M; Lipman, J; Franklin, D; Piccardi, M

SPAD – A Secure and Privacy-Preserving Distributed Analytics Framework

Makhdoom, I

Abolhasan, M

Lipman, J

Franklin, D

Piccardi, M

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Conference Proceeding
Citation:: 2025 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), 2025, 00, pp. 1-3
Issue Date:: 2025-06-06

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Field	Value	Language
dc.contributor.author	Makhdoom, I https://orcid.org/0000-0002-6205-5897
dc.contributor.author	Abolhasan, M https://orcid.org/0000-0002-4282-6666
dc.contributor.author	Lipman, J https://orcid.org/0000-0003-2877-1168
dc.contributor.author	Franklin, D https://orcid.org/0000-0002-9563-5943
dc.contributor.author	Piccardi, M
dc.date	2025-06-02
dc.date.accessioned	2025-11-20T23:45:56Z
dc.date.available	2025-11-20T23:45:56Z
dc.date.issued	2025-06-06
dc.identifier.citation	2025 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), 2025, 00, pp. 1-3
dc.identifier.uri	http://hdl.handle.net/10453/190704
dc.description.abstract	Conventional secure multi-party computation schemes depend on trusted parties and have high complexity, transparency, and data integrity concerns. This research presents SPAD, a secure, distributed, and privacy-preserving data storage and analytics framework designed to address the above challenges. SPAD leverages Shamir’s secret-sharing scheme, Paillier Homomorphic Encryption, and Distributed Ledger Technology to ensure the confidentiality and integrity of data, security of private keys, low communication overheads, transparency and better scalability. It also facilitates building a verifiable authentic dataset to augment accurate analytics. The security analysis determines how SPAD mitigates the threats of privacy leakage and malicious collusion and facilitates privacypreserving analytics. The experimental results, comprising execution time and memory usage, complement the efficient performance of the framework.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation	FA117
dc.relation.ispartof	2025 IEEE International Conference on Blockchain and Cryptocurrency (ICBC)
dc.relation.ispartof	2025 IEEE International Conference on Blockchain and Cryptocurrency (ICBC)
dc.relation.isbasedon	10.1109/icbc64466.2025.11114453
dc.rights	info:eu-repo/semantics/openAccess
dc.title	SPAD – A Secure and Privacy-Preserving Distributed Analytics Framework
dc.type	Conference Proceeding
utslib.citation.volume	00
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Global Big Data Technologies Centre (GBDTC)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Transport Research Centre (TRC)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Cyber Digital Centre (CDC)
pubs.organisational-group	University of Technology Sydney/UTS Groups/The Trustworthy Digital Society
utslib.copyright.status	open_access	*
dc.date.updated	2025-11-20T23:45:54Z
pubs.finish-date	2025-06-06
pubs.publication-status	Published
pubs.start-date	2025-06-02
pubs.volume	00

Abstract:

Conventional secure multi-party computation schemes depend on trusted parties and have high complexity, transparency, and data integrity concerns. This research presents SPAD, a secure, distributed, and privacy-preserving data storage and analytics framework designed to address the above challenges. SPAD leverages Shamir’s secret-sharing scheme, Paillier Homomorphic Encryption, and Distributed Ledger Technology to ensure the confidentiality and integrity of data, security of private keys, low communication overheads, transparency and better scalability. It also facilitates building a verifiable authentic dataset to augment accurate analytics. The security analysis determines how SPAD mitigates the threats of privacy leakage and malicious collusion and facilitates privacypreserving analytics. The experimental results, comprising execution time and memory usage, complement the efficient performance of the framework.

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http://hdl.handle.net/10453/190704