NDSS 2025 – Understanding Data Importance In Machine Learning Attacks
研究探讨了机器学习攻击中数据的重要性及其潜在风险。通过分析五种攻击类型发现,高价值数据在成员推断和模型窃取等攻击中更易受损,并提出改进防御机制以平衡模型 utility 和数据安全。
2026-1-2 20:0:0
Author: securityboulevard.com(查看原文)
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Session 7D: ML Security
Authors, Creators & Presenters: Rui Wen (CISPA Helmholtz Center for Information Security), Michael Backes (CISPA Helmholtz Center for Information Security), Yang Zhang (CISPA Helmholtz Center for Information Security)
PAPER
Understanding Data Importance in Machine Learning Attacks: Does Valuable Data Pose Greater Harm?
Machine learning has revolutionized numerous domains, playing a crucial role in driving advancements and enabling data-centric processes. The significance of data in training models and shaping their performance cannot be overstated. Recent research has highlighted the heterogeneous impact of individual data samples, particularly the presence of valuable data that significantly contributes to the utility and effectiveness of machine learning models. However, a critical question remains unanswered: are these valuable data samples more vulnerable to machine learning attacks? In this work, we investigate the relationship between data importance and machine learning attacks by analyzing five distinct attack types. Our findings reveal notable insights. For example, we observe that high importance data samples exhibit increased vulnerability in certain attacks, such as membership inference and model stealing. These findings also carry practical implications, inspiring researchers to design more efficient attacks. By analyzing the linkage between membership inference vulnerability and data importance, we demonstrate that sample characteristics can be integrated into membership metrics by introducing sample-specific criteria, therefore enhancing the membership inference performance. These findings emphasize the urgent need for innovative defense mechanisms that strike a balance between maximizing utility and safeguarding valuable data against potential exploitation.
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The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.
