Citation

Chew, Yee Jian and Ooi, Shih Yin and Pang, Ying Han and Lim, Zheng You (2025) Decision Tree Pruning with Privacy-Preserving Strategies. Electronics (Switzerland), 14 (15). p. 3139. ISSN 2079-9292

Text electronics-14-03139-v2.pdf - Published Version Restricted to Repository staff only Download (3MB)

Abstract

Machine learning techniques, particularly decision trees, have been extensively utilized in Network-based Intrusion Detection Systems (NIDSs) due to their transparent, rule-based structures that enable straightforward interpretation. However, this transparency presents privacy risks, as decision trees may inadvertently expose sensitive information such as network configurations or IP addresses. In our previous work, we introduced a sensitive pruning-based decision tree to mitigate these risks within a limited dataset and basic pruning framework. In this extended study, three privacy-preserving pruning strategies are proposed: standard sensitive pruning, which conceals specific sensitive attribute values; optimistic sensitive pruning, which further simplifies the decision tree when the sensitive splits are minimal; and pessimistic sensitive pruning, which aggressively removes entire subtrees to maximize privacy protection. The methods are implemented using the J48 (Weka C4.5 package) decision tree algorithm and are rigorously validated across three full-scale NIDS datasets: GureKDDCup, UNSW-NB15, and CIDDS-001. To ensure a realistic evaluation of time-dependent intrusion patterns, a rolling-origin resampling scheme is employed in place of conventional cross-validation. Additionally, IP address truncation and port bilateral classification are incorporated to further enhance privacy preservation. Experimental results demonstrate that the proposed pruning strategies effectively reduce the exposure of sensitive information, significantly simplify decision tree structures, and incur only minimal reductions in classification accuracy. These findings reaffirm that privacy protection can be successfully integrated into decision tree models without severely compromising detection performance. To further support the proposed pruning strategies, this study also includes a comprehensive review of decision tree post-pruning techniques.

Item Type:	Article
Uncontrolled Keywords:	Privacy-preserving, machine learning
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	27 Aug 2025 03:35
Last Modified:	03 Sep 2025 09:18
URII:	http://shdl.mmu.edu.my/id/eprint/14427

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