Try to Avoid Attacks: A Federated Data Sanitization Defense for Healthcare IoMT Systems

• URL: http://arxiv.org/abs/2211.01592v1
• Date: Thu, 3 Nov 2022 05:21:39 GMT
• Title: Try to Avoid Attacks: A Federated Data Sanitization Defense for Healthcare IoMT Systems
• Authors: Chong Chen, Ying Gao, Leyu Shi, Siquan Huang
• Abstract summary: The distribution of IoMT has the risk of protection from data poisoning attacks. Poisoned data can be fabricated by falsifying medical data. This paper introduces a Federated Data Sanitization Defense, a novel approach to protect the system from data poisoning attacks.
• Score: 4.024567343465081
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Healthcare IoMT systems are becoming intelligent, miniaturized, and more integrated into daily life. As for the distributed devices in the IoMT, federated learning has become a topical area with cloud-based training procedures when meeting data security. However, the distribution of IoMT has the risk of protection from data poisoning attacks. Poisoned data can be fabricated by falsifying medical data, which urges a security defense to IoMT systems. Due to the lack of specific labels, the filtering of malicious data is a unique unsupervised scenario. One of the main challenges is finding robust data filtering methods for various poisoning attacks. This paper introduces a Federated Data Sanitization Defense, a novel approach to protect the system from data poisoning attacks. To solve this unsupervised problem, we first use federated learning to project all the data to the subspace domain, allowing unified feature mapping to be established since the data is stored locally. Then we adopt the federated clustering to re-group their features to clarify the poisoned data. The clustering is based on the consistent association of data and its semantics. After we get the clustering of the private data, we do the data sanitization with a simple yet efficient strategy. In the end, each device of distributed ImOT is enabled to filter malicious data according to federated data sanitization. Extensive experiments are conducted to evaluate the efficacy of the proposed defense method against data poisoning attacks. Further, we consider our approach in the different poisoning ratios and achieve a high Accuracy and a low attack success rate.

Related papers

• A GAN-Based Data Poisoning Attack Against Federated Learning Systems and Its Countermeasure [17.975736855580674]
  This paper presents a new data poisoning attack model named VagueGAN. VagueGAN can generate seemingly legitimate but noisy poisoned data by taking advantage of generative adversarial network (GAN) variants. Our attack method is generally much more stealthy as well as more effective in degrading FL performance with low complexity.
  arXiv  Detail & Related papers  (2024-05-19T04:23:40Z)
• Towards Attack-tolerant Federated Learning via Critical Parameter Analysis [85.41873993551332]
  Federated learning systems are susceptible to poisoning attacks when malicious clients send false updates to the central server. This paper proposes a new defense strategy, FedCPA (Federated learning with Critical Analysis) Our attack-tolerant aggregation method is based on the observation that benign local models have similar sets of top-k and bottom-k critical parameters, whereas poisoned local models do not.
  arXiv  Detail & Related papers  (2023-08-18T05:37:55Z)
• Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigations [55.2480439325792]
  Federated Learning (FL) facilitates decentralized machine learning model training, preserving data privacy, lowering communication costs, and boosting model performance through diversified data sources. FL faces vulnerabilities such as poisoning attacks, undermining model integrity with both untargeted performance degradation and targeted backdoor attacks. We define a new notion of strong adaptive adversaries, capable of adapting to multiple objectives simultaneously. MESAS is the first defense robust against strong adaptive adversaries, effective in real-world data scenarios, with an average overhead of just 24.37 seconds.
  arXiv  Detail & Related papers  (2023-06-06T11:44:42Z)
• Stop Uploading Test Data in Plain Text: Practical Strategies for Mitigating Data Contamination by Evaluation Benchmarks [70.39633252935445]
  Data contamination has become prevalent and challenging with the rise of models pretrained on large automatically-crawled corpora. For closed models, the training data becomes a trade secret, and even for open models, it is not trivial to detect contamination. We propose three strategies that can make a difference: (1) Test data made public should be encrypted with a public key and licensed to disallow derivative distribution; (2) demand training exclusion controls from closed API holders, and protect your test data by refusing to evaluate without them; and (3) avoid data which appears with its solution on the internet, and release the web-page context of internet-derived
  arXiv  Detail & Related papers  (2023-05-17T12:23:38Z)
• Autoregressive Perturbations for Data Poisoning [54.205200221427994]
  Data scraping from social media has led to growing concerns regarding unauthorized use of data. Data poisoning attacks have been proposed as a bulwark against scraping. We introduce autoregressive (AR) poisoning, a method that can generate poisoned data without access to the broader dataset.
  arXiv  Detail & Related papers  (2022-06-08T06:24:51Z)
• A Data Quarantine Model to Secure Data in Edge Computing [0.0]
  Edge computing provides an agile data processing platform for latency-sensitive and communication-intensive applications. Data integrity attacks can lead to inconsistent data and intrude edge data analytics. This paper proposes a new concept of data quarantine model to mitigate data integrity attacks by quarantining intruders.
  arXiv  Detail & Related papers  (2021-11-15T11:04:48Z)
• Accumulative Poisoning Attacks on Real-time Data [56.96241557830253]
  We show that a well-designed but straightforward attacking strategy can dramatically amplify the poisoning effects. Our work validates that a well-designed but straightforward attacking strategy can dramatically amplify the poisoning effects.
  arXiv  Detail & Related papers  (2021-06-18T08:29:53Z)
• Influence Based Defense Against Data Poisoning Attacks in Online Learning [9.414651358362391]
  Data poisoning is an attack where an attacker manipulates a fraction of data to degrade the performance of machine learning model. We propose a defense mechanism to minimize the degradation caused by the poisoned training data on a learner's model in an online setup.
  arXiv  Detail & Related papers  (2021-04-24T08:39:13Z)
• Defening against Adversarial Denial-of-Service Attacks [0.0]
  Data poisoning is one of the most relevant security threats against machine learning and data-driven technologies. We propose a new approach of detecting DoS poisoned instances. We evaluate our defence against two DoS poisoning attacks and seven datasets, and find that it reliably identifies poisoned instances.
  arXiv  Detail & Related papers  (2021-04-14T09:52:36Z)
• Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks [74.88735178536159]
  Data poisoning is the number one concern among threats ranging from model stealing to adversarial attacks. We observe that data poisoning and backdoor attacks are highly sensitive to variations in the testing setup. We apply rigorous tests to determine the extent to which we should fear them.
  arXiv  Detail & Related papers  (2020-06-22T18:34:08Z)

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.