Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning

• URL: http://arxiv.org/abs/2411.15796v1
• Date: Sun, 24 Nov 2024 11:46:59 GMT
• Title: Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning
• Authors: Qi Li, Cheng-Long Wang, Yinzhi Cao, Di Wang,
• Abstract summary: We show that even if data in a redundant set is solely used before model training, its pruning-phase membership status can still be detected through attacks. We introduce a new task called Data-Centric Membership Inference and propose the first ever data-centric privacy inference paradigm named Data Lineage Inference. We find that different pruning methods involve varying levels of privacy leakage, and even the same pruning method can present different privacy risks at different pruning fractions.
• Score: 31.888075470799908
• License:
• Abstract: In this work, we systematically explore the data privacy issues of dataset pruning in machine learning systems. Our findings reveal, for the first time, that even if data in the redundant set is solely used before model training, its pruning-phase membership status can still be detected through attacks. Since this is a fully upstream process before model training, traditional model output-based privacy inference methods are completely unsuitable. To address this, we introduce a new task called Data-Centric Membership Inference and propose the first ever data-centric privacy inference paradigm named Data Lineage Inference (DaLI). Under this paradigm, four threshold-based attacks are proposed, named WhoDis, CumDis, ArraDis and SpiDis. We show that even without access to downstream models, adversaries can accurately identify the redundant set with only limited prior knowledge. Furthermore, we find that different pruning methods involve varying levels of privacy leakage, and even the same pruning method can present different privacy risks at different pruning fractions. We conducted an in-depth analysis of these phenomena and introduced a metric called the Brimming score to offer guidance for selecting pruning methods with privacy protection in mind.

Related papers

• Pseudo-Probability Unlearning: Towards Efficient and Privacy-Preserving Machine Unlearning [59.29849532966454]
  We propose PseudoProbability Unlearning (PPU), a novel method that enables models to forget data to adhere to privacy-preserving manner. Our method achieves over 20% improvements in forgetting error compared to the state-of-the-art.
  arXiv  Detail & Related papers  (2024-11-04T21:27:06Z)
• Initialization Matters: Privacy-Utility Analysis of Overparameterized Neural Networks [72.51255282371805]
  We prove a privacy bound for the KL divergence between model distributions on worst-case neighboring datasets. We find that this KL privacy bound is largely determined by the expected squared gradient norm relative to model parameters during training.
  arXiv  Detail & Related papers  (2023-10-31T16:13:22Z)
• Conditional Density Estimations from Privacy-Protected Data [0.0]
  We propose simulation-based inference methods from privacy-protected datasets. We illustrate our methods on discrete time-series data under an infectious disease model and with ordinary linear regression models.
  arXiv  Detail & Related papers  (2023-10-19T14:34:17Z)
• A Unified View of Differentially Private Deep Generative Modeling [60.72161965018005]
  Data with privacy concerns comes with stringent regulations that frequently prohibited data access and data sharing. Overcoming these obstacles is key for technological progress in many real-world application scenarios that involve privacy sensitive data. Differentially private (DP) data publishing provides a compelling solution, where only a sanitized form of the data is publicly released.
  arXiv  Detail & Related papers  (2023-09-27T14:38:16Z)
• Differentially Private Linear Regression with Linked Data [3.9325957466009203]
  Differential privacy, a mathematical notion from computer science, is a rising tool offering robust privacy guarantees. Recent work focuses on developing differentially private versions of individual statistical and machine learning tasks. We present two differentially private algorithms for linear regression with linked data.
  arXiv  Detail & Related papers  (2023-08-01T21:00:19Z)
• SoK: Privacy-Preserving Data Synthesis [72.92263073534899]
  This paper focuses on privacy-preserving data synthesis (PPDS) by providing a comprehensive overview, analysis, and discussion of the field. We put forth a master recipe that unifies two prominent strands of research in PPDS: statistical methods and deep learning (DL)-based methods.
  arXiv  Detail & Related papers  (2023-07-05T08:29:31Z)
• Client-specific Property Inference against Secure Aggregation in Federated Learning [52.8564467292226]
  Federated learning has become a widely used paradigm for collaboratively training a common model among different participants. Many attacks have shown that it is still possible to infer sensitive information such as membership, property, or outright reconstruction of participant data. We show that simple linear models can effectively capture client-specific properties only from the aggregated model updates.
  arXiv  Detail & Related papers  (2023-03-07T14:11:01Z)
• Learning to Unlearn: Instance-wise Unlearning for Pre-trained Classifiers [71.70205894168039]
  We consider instance-wise unlearning, of which the goal is to delete information on a set of instances from a pre-trained model. We propose two methods that reduce forgetting on the remaining data: 1) utilizing adversarial examples to overcome forgetting at the representation-level and 2) leveraging weight importance metrics to pinpoint network parameters guilty of propagating unwanted information.
  arXiv  Detail & Related papers  (2023-01-27T07:53:50Z)
• A General Framework for Auditing Differentially Private Machine Learning [27.99806936918949]
  We present a framework to statistically audit the privacy guarantee conferred by a differentially private machine learner in practice. Our work develops a general methodology to empirically evaluate the privacy of differentially private machine learning implementations.
  arXiv  Detail & Related papers  (2022-10-16T21:34:18Z)
• Uncertainty-Autoencoder-Based Privacy and Utility Preserving Data Type Conscious Transformation [3.7315964084413173]
  We propose an adversarial learning framework that deals with the privacy-utility tradeoff problem under two conditions. Under data-type ignorant conditions, the privacy mechanism provides a one-hot encoding of categorical features, representing exactly one class. Under data-type aware conditions, the categorical variables are represented by a collection of scores, one for each class.
  arXiv  Detail & Related papers  (2022-05-04T08:40:15Z)
• Data-driven Regularized Inference Privacy [33.71757542373714]
  We propose a data-driven inference privacy preserving framework to sanitize data. We develop an inference privacy framework based on the variational method. We present empirical methods to estimate the privacy metric.
  arXiv  Detail & Related papers  (2020-10-10T08:42:59Z)

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.