Cost-Efficient and Privacy-Preserving Synthesis of Complex Sensitive Data

Suresh, S; Zhang, G; Liu, B; Drake, B

Cost-Efficient and Privacy-Preserving Synthesis of Complex Sensitive Data

Suresh, S

Zhang, G

Liu, B

Drake, B

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Publisher:: Association for Computing Machinery (ACM)
Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2025 Australasian Computer Science Week, 2025, pp. 30-39
Issue Date:: 2025-02-10

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Field	Value	Language
dc.contributor.author	Suresh, S https://orcid.org/0000-0003-2443-587X
dc.contributor.author	Zhang, G https://orcid.org/0000-0002-9776-0529
dc.contributor.author	Liu, B https://orcid.org/0000-0002-3603-6617
dc.contributor.author	Drake, B https://orcid.org/0000-0003-0572-9936
dc.date.accessioned	2025-07-08T01:51:59Z
dc.date.available	2025-07-08T01:51:59Z
dc.date.issued	2025-02-10
dc.identifier.citation	Proceedings of the 2025 Australasian Computer Science Week, 2025, pp. 30-39
dc.identifier.uri	http://hdl.handle.net/10453/188109
dc.description.abstract	This paper introduces a novel method for generating differentially private synthetic datasets that harnesses Bayesian networks to ensure the preservation of essential statistical properties and referential integrity across linked tables. To address the dual challenges of maintaining privacy and minimizing computational overhead, we introduce a decomposition scheme for additive Laplacian noise that significantly reduces computational costs while enhancing the efficiency of the differential privacy framework. Our methodology offers a robust solution for creating synthetic datasets that not only mimic the statistical characteristics of original datasets, but also safeguard sensitive information against inference attacks. Through comprehensive evaluations, we demonstrate the practicality and effectiveness of our approach, which achieves a significant speedup in noise injection, thereby facilitating real-time data analysis. This breakthrough contributes to the broader accessibility of complex data analysis, particularly benefiting sectors dealing with sensitive information by improving data privacy and security measures. Our findings represent a significant advancement in statistical methodologies and software, underscoring the ongoing necessity for innovation in data processing techniques.
dc.language	en
dc.publisher	Association for Computing Machinery (ACM)
dc.relation	DP230100246
dc.relation	http://purl.org/au-research/grants/arc/DP230100246
dc.relation	http://purl.org/au-research/grants/arc/LP220200808
dc.relation	DP250100463
dc.relation.ispartof	Proceedings of the 2025 Australasian Computer Science Week
dc.relation.ispartof	Proceedings of the 2025 Australasian Computer Science Week
dc.relation.isbasedon	10.1145/3727166.3727170
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Cost-Efficient and Privacy-Preserving Synthesis of Complex Sensitive Data
dc.type	Conference Proceeding
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 Computer Science
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Australian Artificial Intelligence Institute (AAII)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Health Technologies (CHT)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Data Science Institute (DSI)
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-07-08T01:51:57Z
pubs.publication-status	Published

Abstract:

This paper introduces a novel method for generating differentially private synthetic datasets that harnesses Bayesian networks to ensure the preservation of essential statistical properties and referential integrity across linked tables. To address the dual challenges of maintaining privacy and minimizing computational overhead, we introduce a decomposition scheme for additive Laplacian noise that significantly reduces computational costs while enhancing the efficiency of the differential privacy framework. Our methodology offers a robust solution for creating synthetic datasets that not only mimic the statistical characteristics of original datasets, but also safeguard sensitive information against inference attacks. Through comprehensive evaluations, we demonstrate the practicality and effectiveness of our approach, which achieves a significant speedup in noise injection, thereby facilitating real-time data analysis. This breakthrough contributes to the broader accessibility of complex data analysis, particularly benefiting sectors dealing with sensitive information by improving data privacy and security measures. Our findings represent a significant advancement in statistical methodologies and software, underscoring the ongoing necessity for innovation in data processing techniques.

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http://hdl.handle.net/10453/188109