$k$-Anonymity in Practice: How Generalisation and Suppression Affect Machine Learning Classifiers

• URL: http://arxiv.org/abs/2102.04763v1
• Date: Tue, 9 Feb 2021 11:28:20 GMT
• Title: $k$-Anonymity in Practice: How Generalisation and Suppression Affect Machine Learning Classifiers
• Authors: Djordje Slijep\v{c}evi\'c, Maximilian Henzl, Lukas Daniel Klausner, Tobias Dam, Peter Kieseberg, Matthias Zeppelzauer
• Abstract summary: We investigate the effects of different $k$-anonymisation algorithms on the results of machine learning models. Our systematic evaluation shows that with an increasingly strong $k$-anonymity constraint, the classification performance generally degrades. Mondrian can be considered as the method with the most appealing properties for subsequent classification.
• Score: 2.4282642968872037
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The protection of private information is a crucial issue in data-driven research and business contexts. Typically, techniques like anonymisation or (selective) deletion are introduced in order to allow data sharing, \eg\ in the case of collaborative research endeavours. For use with anonymisation techniques, the $k$-anonymity criterion is one of the most popular, with numerous scientific publications on different algorithms and metrics. Anonymisation techniques often require changing the data and thus necessarily affect the results of machine learning models trained on the underlying data. In this work, we conduct a systematic comparison and detailed investigation into the effects of different $k$-anonymisation algorithms on the results of machine learning models. We investigate a set of popular $k$-anonymisation algorithms with different classifiers and evaluate them on different real-world datasets. Our systematic evaluation shows that with an increasingly strong $k$-anonymity constraint, the classification performance generally degrades, but to varying degrees and strongly depending on the dataset and anonymisation method. Furthermore, Mondrian can be considered as the method with the most appealing properties for subsequent classification.

Related papers

• Approaching Metaheuristic Deep Learning Combos for Automated Data Mining [0.5419570023862531]
  This work proposes a means of combining meta-heuristic methods with conventional classifiers and neural networks in order to perform automated data mining. Experiments on the MNIST dataset for handwritten digit recognition were performed. It was empirically observed that using a ground truth labeled dataset's validation accuracy is inadequate for correcting labels of other previously unseen data instances.
  arXiv  Detail & Related papers  (2024-10-16T10:28:22Z)
• Gone but Not Forgotten: Improved Benchmarks for Machine Unlearning [0.0]
  We describe and propose alternative evaluation methods for machine unlearning algorithms. We show the utility of our alternative evaluations via a series of experiments of state-of-the-art unlearning algorithms on different computer vision datasets.
  arXiv  Detail & Related papers  (2024-05-29T15:53:23Z)
• How to Leverage Diverse Demonstrations in Offline Imitation Learning [39.24627312800116]
  Offline Imitation Learning (IL) with imperfect demonstrations has garnered increasing attention owing to the scarcity of expert data. We introduce a simple yet effective data selection method that identifies positive behaviors based on their resultant states. We then devise a lightweight behavior cloning algorithm capable of leveraging the expert and selected data correctly.
  arXiv  Detail & Related papers  (2024-05-24T04:56:39Z)
• Comparison of machine learning models applied on anonymized data with different techniques [0.0]
  We study four classical machine learning methods currently used for classification purposes in order to analyze the results as a function of the anonymization techniques applied and the parameters selected for each of them. The performance of these models is studied when varying the value of k for k-anonymity and additional tools such as $ell$-diversity, t-closeness and $delta$-disclosure privacy are also deployed on the well-known adult dataset.
  arXiv  Detail & Related papers  (2023-05-12T12:34:07Z)
• Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-based Recruitment [66.91538273487379]
  There is a certain consensus about the need to develop AI applications with a Human-Centric approach. Human-Centric Machine Learning needs to be developed based on four main requirements: (i) utility and social good; (ii) privacy and data ownership; (iii) transparency and accountability; and (iv) fairness in AI-driven decision-making processes. We study how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data.
  arXiv  Detail & Related papers  (2023-02-13T16:44:44Z)
• Debiased Pseudo Labeling in Self-Training [77.83549261035277]
  Deep neural networks achieve remarkable performances on a wide range of tasks with the aid of large-scale labeled datasets. To mitigate the requirement for labeled data, self-training is widely used in both academia and industry by pseudo labeling on readily-available unlabeled data. We propose Debiased, in which the generation and utilization of pseudo labels are decoupled by two independent heads.
  arXiv  Detail & Related papers  (2022-02-15T02:14:33Z)
• Leveraging Ensembles and Self-Supervised Learning for Fully-Unsupervised Person Re-Identification and Text Authorship Attribution [77.85461690214551]
  Learning from fully-unlabeled data is challenging in Multimedia Forensics problems, such as Person Re-Identification and Text Authorship Attribution. Recent self-supervised learning methods have shown to be effective when dealing with fully-unlabeled data in cases where the underlying classes have significant semantic differences. We propose a strategy to tackle Person Re-Identification and Text Authorship Attribution by enabling learning from unlabeled data even when samples from different classes are not prominently diverse.
  arXiv  Detail & Related papers  (2022-02-07T13:08:11Z)
• Selecting the suitable resampling strategy for imbalanced data classification regarding dataset properties [62.997667081978825]
  In many application domains such as medicine, information retrieval, cybersecurity, social media, etc., datasets used for inducing classification models often have an unequal distribution of the instances of each class. This situation, known as imbalanced data classification, causes low predictive performance for the minority class examples. Oversampling and undersampling techniques are well-known strategies to deal with this problem by balancing the number of examples of each class.
  arXiv  Detail & Related papers  (2021-12-15T18:56:39Z)
• Low-rank Dictionary Learning for Unsupervised Feature Selection [11.634317251468968]
  We introduce a novel unsupervised feature selection approach by applying dictionary learning ideas in a low-rank representation. A unified objective function for unsupervised feature selection is proposed in a sparse way by an $ell_2,1$-norm regularization. Our experimental findings reveal that the proposed method outperforms the state-of-the-art algorithm.
  arXiv  Detail & Related papers  (2021-06-21T13:39:10Z)
• DomainMix: Learning Generalizable Person Re-Identification Without Human Annotations [89.78473564527688]
  This paper shows how to use labeled synthetic dataset and unlabeled real-world dataset to train a universal model. In this way, human annotations are no longer required, and it is scalable to large and diverse real-world datasets. Experimental results show that the proposed annotation-free method is more or less comparable to the counterpart trained with full human annotations.
  arXiv  Detail & Related papers  (2020-11-24T08:15:53Z)
• Bias in Multimodal AI: Testbed for Fair Automatic Recruitment [73.85525896663371]
  We study how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data. We train automatic recruitment algorithms using a set of multimodal synthetic profiles consciously scored with gender and racial biases. Our methodology and results show how to generate fairer AI-based tools in general, and in particular fairer automated recruitment systems.
  arXiv  Detail & Related papers  (2020-04-15T15:58:05Z)

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.