Understanding Data Importance in Machine Learning Attacks: Does Valuable Data Pose Greater Harm?

• URL: http://arxiv.org/abs/2409.03741v1
• Date: Thu, 5 Sep 2024 17:54:26 GMT
• Title: Understanding Data Importance in Machine Learning Attacks: Does Valuable Data Pose Greater Harm?
• Authors: Rui Wen, Michael Backes, Yang Zhang,
• Abstract summary: We investigate the relationship between data importance and machine learning attacks by analyzing five distinct attack types. For example, we observe that high importance data samples exhibit increased vulnerability in certain attacks, such as membership inference and model stealing. These findings emphasize the urgent need for innovative defense mechanisms that strike a balance between maximizing utility and safeguarding valuable data.
• Score: 23.2883929808036
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. 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.

Related papers

• Releasing Malevolence from Benevolence: The Menace of Benign Data on Machine Unlearning [28.35038726318893]
  Machine learning models trained on vast amounts of real or synthetic data often achieve outstanding predictive performance across various domains. To address privacy concerns, machine unlearning has been proposed to erase specific data samples from models. We introduce the Unlearning Usability Attack to distill data distribution information into a small set of benign data.
  arXiv  Detail & Related papers  (2024-07-06T15:42:28Z)
• Silver Linings in the Shadows: Harnessing Membership Inference for Machine Unlearning [7.557226714828334]
  We present a novel unlearning mechanism designed to remove the impact of specific data samples from a neural network. In achieving this goal, we crafted a novel loss function tailored to eliminate privacy-sensitive information from weights and activation values of the target model. Our results showcase the superior performance of our approach in terms of unlearning efficacy and latency as well as the fidelity of the primary task.
  arXiv  Detail & Related papers  (2024-07-01T00:20:26Z)
• Efficient Data-Free Model Stealing with Label Diversity [22.8804507954023]
  Machine learning as a Service (ML) allows users to query the machine learning model in an API manner, which provides an opportunity for users to enjoy the benefits brought by the high-performance model trained on valuable data. This interface boosts the proliferation of machine learning based applications, while on the other hand, it introduces the attack surface for model stealing attacks. Existing model stealing attacks have relaxed their attack assumptions to the data-free setting, while keeping the effectiveness. In this paper, we revisit the model stealing problem from a diversity perspective and demonstrate that keeping the generated data samples more diverse across all the classes is the critical point
  arXiv  Detail & Related papers  (2024-03-29T18:52:33Z)
• It Is All About Data: A Survey on the Effects of Data on Adversarial Robustness [4.1310970179750015]
  Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to confuse the model into making a mistake. To address this problem, the area of adversarial robustness investigates mechanisms behind adversarial attacks and defenses against these attacks.
  arXiv  Detail & Related papers  (2023-03-17T04:18:03Z)
• Striving for data-model efficiency: Identifying data externalities on group performance [75.17591306911015]
  Building trustworthy, effective, and responsible machine learning systems hinges on understanding how differences in training data and modeling decisions interact to impact predictive performance. We focus on a particular type of data-model inefficiency, in which adding training data from some sources can actually lower performance evaluated on key sub-groups of the population. Our results indicate that data-efficiency is a key component of both accurate and trustworthy machine learning.
  arXiv  Detail & Related papers  (2022-11-11T16:48:27Z)
• Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets [53.866927712193416]
  We show that an adversary who can poison a training dataset can cause models trained on this dataset to leak private details belonging to other parties. Our attacks are effective across membership inference, attribute inference, and data extraction. Our results cast doubts on the relevance of cryptographic privacy guarantees in multiparty protocols for machine learning.
  arXiv  Detail & Related papers  (2022-03-31T18:06:28Z)
• Quantifying and Mitigating Privacy Risks of Contrastive Learning [4.909548818641602]
  We perform the first privacy analysis of contrastive learning through the lens of membership inference and attribute inference. Our results show that contrastive models are less vulnerable to membership inference attacks but more vulnerable to attribute inference attacks compared to supervised models. To remedy this situation, we propose the first privacy-preserving contrastive learning mechanism, namely Talos.
  arXiv  Detail & Related papers  (2021-02-08T11:38:11Z)
• ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models [64.03398193325572]
  Inference attacks against Machine Learning (ML) models allow adversaries to learn about training data, model parameters, etc. We concentrate on four attacks - namely, membership inference, model inversion, attribute inference, and model stealing. Our analysis relies on a modular re-usable software, ML-Doctor, which enables ML model owners to assess the risks of deploying their models.
  arXiv  Detail & Related papers  (2021-02-04T11:35:13Z)
• Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning [51.15273664903583]
  Data heterogeneity has been identified as one of the key features in federated learning but often overlooked in the lens of robustness to adversarial attacks. This paper focuses on characterizing and understanding its impact on backdooring attacks in federated learning through comprehensive experiments using synthetic and the LEAF benchmarks.
  arXiv  Detail & Related papers  (2021-02-01T06:06:21Z)
• Sampling Attacks: Amplification of Membership Inference Attacks by Repeated Queries [74.59376038272661]
  We introduce sampling attack, a novel membership inference technique that unlike other standard membership adversaries is able to work under severe restriction of no access to scores of the victim model. We show that a victim model that only publishes the labels is still susceptible to sampling attacks and the adversary can recover up to 100% of its performance. For defense, we choose differential privacy in the form of gradient perturbation during the training of the victim model as well as output perturbation at prediction time.
  arXiv  Detail & Related papers  (2020-09-01T12:54:54Z)
• Provably Efficient Causal Reinforcement Learning with Confounded Observational Data [135.64775986546505]
  We study how to incorporate the dataset (observational data) collected offline, which is often abundantly available in practice, to improve the sample efficiency in the online setting. We propose the deconfounded optimistic value iteration (DOVI) algorithm, which incorporates the confounded observational data in a provably efficient manner.
  arXiv  Detail & Related papers  (2020-06-22T14:49:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.