Feature Selection from Differentially Private Correlations

• URL: http://arxiv.org/abs/2408.10862v2
• Date: Fri, 23 Aug 2024 03:03:57 GMT
• Title: Feature Selection from Differentially Private Correlations
• Authors: Ryan Swope, Amol Khanna, Philip Doldo, Saptarshi Roy, Edward Raff,
• Abstract summary: High-dimensional regression can leak information about individual datapoints in a dataset. We employ a correlations-based order statistic to choose important features from a dataset and privatize them. We find that our method significantly outperforms the established baseline for private feature selection on many datasets.
• Score: 35.187113265093615
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data scientists often seek to identify the most important features in high-dimensional datasets. This can be done through $L_1$-regularized regression, but this can become inefficient for very high-dimensional datasets. Additionally, high-dimensional regression can leak information about individual datapoints in a dataset. In this paper, we empirically evaluate the established baseline method for feature selection with differential privacy, the two-stage selection technique, and show that it is not stable under sparsity. This makes it perform poorly on real-world datasets, so we consider a different approach to private feature selection. We employ a correlations-based order statistic to choose important features from a dataset and privatize them to ensure that the results do not leak information about individual datapoints. We find that our method significantly outperforms the established baseline for private feature selection on many datasets.

Related papers

• A CLIP-Powered Framework for Robust and Generalizable Data Selection [51.46695086779598]
  Real-world datasets often contain redundant and noisy data, imposing a negative impact on training efficiency and model performance. Data selection has shown promise in identifying the most representative samples from the entire dataset. We propose a novel CLIP-powered data selection framework that leverages multimodal information for more robust and generalizable sample selection.
  arXiv  Detail & Related papers  (2024-10-15T03:00:58Z)
• Adapt-$\infty$: Scalable Lifelong Multimodal Instruction Tuning via Dynamic Data Selection [89.42023974249122]
  Adapt-$infty$ is a new multi-way and adaptive data selection approach for Lifelong Instruction Tuning. We construct pseudo-skill clusters by grouping gradient-based sample vectors. We select the best-performing data selector for each skill cluster from a pool of selector experts.
  arXiv  Detail & Related papers  (2024-10-14T15:48:09Z)
• Rethinking Data Selection at Scale: Random Selection is Almost All You Need [39.14807071480125]
  Supervised fine-tuning is crucial for aligning Large Language Models with human instructions. Most existing data selection techniques are designed for small-scale data pools.
  arXiv  Detail & Related papers  (2024-10-12T02:48:34Z)
• Privacy-Optimized Randomized Response for Sharing Multi-Attribute Data [1.1510009152620668]
  We propose a privacy-optimized randomized response that guarantees the strongest privacy in sharing multi-attribute data. We also present an efficient algorithm for constructing a near-optimal attribute mechanism. Our methods provide significantly stronger privacy guarantees for the entire dataset than the existing method.
  arXiv  Detail & Related papers  (2024-02-12T11:34:42Z)
• LESS: Selecting Influential Data for Targeted Instruction Tuning [64.78894228923619]
  We propose LESS, an efficient algorithm to estimate data influences and perform Low-rank gradiEnt Similarity Search for instruction data selection. We show that training on a LESS-selected 5% of the data can often outperform training on the full dataset across diverse downstream tasks. Our method goes beyond surface form cues to identify data that the necessary reasoning skills for the intended downstream application.
  arXiv  Detail & Related papers  (2024-02-06T19:18:04Z)
• DsDm: Model-Aware Dataset Selection with Datamodels [81.01744199870043]
  Standard practice is to filter for examples that match human notions of data quality. We find that selecting according to similarity with "high quality" data sources may not increase (and can even hurt) performance compared to randomly selecting data. Our framework avoids handpicked notions of data quality, and instead models explicitly how the learning process uses train datapoints to predict on the target tasks.
  arXiv  Detail & Related papers  (2024-01-23T17:22:00Z)
• Mean Estimation with User-level Privacy under Data Heterogeneity [54.07947274508013]
  Different users may possess vastly different numbers of data points. It cannot be assumed that all users sample from the same underlying distribution. We propose a simple model of heterogeneous user data that allows user data to differ in both distribution and quantity of data.
  arXiv  Detail & Related papers  (2023-07-28T23:02:39Z)
• infoVerse: A Universal Framework for Dataset Characterization with Multidimensional Meta-information [68.76707843019886]
  infoVerse is a universal framework for dataset characterization. infoVerse captures multidimensional characteristics of datasets by incorporating various model-driven meta-information. In three real-world applications (data pruning, active learning, and data annotation), the samples chosen on infoVerse space consistently outperform strong baselines.
  arXiv  Detail & Related papers  (2023-05-30T18:12:48Z)
• Selecting Features by their Resilience to the Curse of Dimensionality [0.0]
  Real-world datasets are often of high dimension and effected by the curse of dimensionality. Here we step in with a novel method that identifies the features that allow to discriminate data subsets of different sizes. Our experiments show that our method is competitive and commonly outperforms established feature selection methods.
  arXiv  Detail & Related papers  (2023-04-05T14:26:23Z)
• Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasets [27.562256973255728]
  Natural language processing models often exploit spurious correlations between task-independent features and labels in datasets to perform well only within the distributions they are trained on. We propose to tackle this problem by generating a debiased version of a dataset, which can then be used to train a debiased, off-the-shelf model. Our approach consists of 1) a method for training data generators to generate high-quality, label-consistent data samples; and 2) a filtering mechanism for removing data points that contribute to spurious correlations.
  arXiv  Detail & Related papers  (2022-03-24T09:08:05Z)
• Differentially Private Simple Linear Regression [2.614403183902121]
  We study algorithms for simple linear regression that satisfy differential privacy. We consider the design of differentially private algorithms for simple linear regression for small datasets. We study the performance of a spectrum of algorithms we adapt to the setting.
  arXiv  Detail & Related papers  (2020-07-10T04:28:43Z)

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.