Private Counterfactual Retrieval

• URL: http://arxiv.org/abs/2410.13812v1
• Date: Thu, 17 Oct 2024 17:45:07 GMT
• Title: Private Counterfactual Retrieval
• Authors: Mohamed Nomeir, Pasan Dissanayake, Shreya Meel, Sanghamitra Dutta, Sennur Ulukus,
• Abstract summary: Transparency and explainability are two extremely important aspects to be considered when employing black-box machine learning models. Providing counterfactual explanations is one way of catering this requirement. We propose multiple schemes inspired by private information retrieval (PIR) techniques.
• Score: 34.11302393278422
• License:
• Abstract: Transparency and explainability are two extremely important aspects to be considered when employing black-box machine learning models in high-stake applications. Providing counterfactual explanations is one way of catering this requirement. However, this also poses a threat to the privacy of both the institution that is providing the explanation as well as the user who is requesting it. In this work, we propose multiple schemes inspired by private information retrieval (PIR) techniques which ensure the \emph{user's privacy} when retrieving counterfactual explanations. We present a scheme which retrieves the \emph{exact} nearest neighbor counterfactual explanation from a database of accepted points while achieving perfect (information-theoretic) privacy for the user. While the scheme achieves perfect privacy for the user, some leakage on the database is inevitable which we quantify using a mutual information based metric. Furthermore, we propose strategies to reduce this leakage to achieve an advanced degree of database privacy. We extend these schemes to incorporate user's preference on transforming their attributes, so that a more actionable explanation can be received. Since our schemes rely on finite field arithmetic, we empirically validate our schemes on real datasets to understand the trade-off between the accuracy and the finite field sizes.

Related papers

• Private Counterfactual Retrieval With Immutable Features [34.11302393278422]
  In a classification task, counterfactual explanations provide the minimum change needed for an input to be classified into a favorable class. We consider the problem of privately retrieving the exact closest counterfactual from a database of accepted samples. We propose two I-PCR schemes by leveraging techniques from private information retrieval (PIR) and characterize their communication costs.
  arXiv  Detail & Related papers  (2024-11-15T18:50:53Z)
• 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)
• Ungeneralizable Examples [70.76487163068109]
  Current approaches to creating unlearnable data involve incorporating small, specially designed noises. We extend the concept of unlearnable data to conditional data learnability and introduce textbfUntextbfGeneralizable textbfExamples (UGEs) UGEs exhibit learnability for authorized users while maintaining unlearnability for potential hackers.
  arXiv  Detail & Related papers  (2024-04-22T09:29:14Z)
• Counterfactual Explanations via Locally-guided Sequential Algorithmic Recourse [13.95253855760017]
  We introduce LocalFACE, a model-agnostic technique that composes feasible and actionable counterfactual explanations. Our explainer preserves the privacy of users by only leveraging data that it specifically requires to construct actionable algorithmic recourse.
  arXiv  Detail & Related papers  (2023-09-08T08:47:23Z)
• Probing the Transition to Dataset-Level Privacy in ML Models Using an Output-Specific and Data-Resolved Privacy Profile [23.05994842923702]
  We study a privacy metric that quantifies the extent to which a model trained on a dataset using a Differential Privacy mechanism is covered" by each of the distributions resulting from training on neighboring datasets. We show that the privacy profile can be used to probe an observed transition to indistinguishability that takes place in the neighboring distributions as $epsilon$ decreases.
  arXiv  Detail & Related papers  (2023-06-27T20:39:07Z)
• On Differential Privacy for Federated Learning in Wireless Systems with Multiple Base Stations [90.53293906751747]
  We consider a federated learning model in a wireless system with multiple base stations and inter-cell interference. We show the convergence behavior of the learning process by deriving an upper bound on its optimality gap. Our proposed scheduler improves the average accuracy of the predictions compared with a random scheduler.
  arXiv  Detail & Related papers  (2022-08-25T03:37:11Z)
• Distributed Machine Learning and the Semblance of Trust [66.1227776348216]
  Federated Learning (FL) allows the data owner to maintain data governance and perform model training locally without having to share their data. FL and related techniques are often described as privacy-preserving. We explain why this term is not appropriate and outline the risks associated with over-reliance on protocols that were not designed with formal definitions of privacy in mind.
  arXiv  Detail & Related papers  (2021-12-21T08:44:05Z)
• A Shuffling Framework for Local Differential Privacy [40.92785300658643]
  ldp deployments are vulnerable to inference attacks as an adversary can link the noisy responses to their identity. An alternative model, shuffle DP, prevents this by shuffling the noisy responses uniformly at random. We show that systematic shuffling of the noisy responses can thwart specific inference attacks while retaining some meaningful data learnability.
  arXiv  Detail & Related papers  (2021-06-11T20:36:23Z)
• SPEED: Secure, PrivatE, and Efficient Deep learning [2.283665431721732]
  We introduce a deep learning framework able to deal with strong privacy constraints. Based on collaborative learning, differential privacy and homomorphic encryption, the proposed approach advances state-of-the-art.
  arXiv  Detail & Related papers  (2020-06-16T19:31:52Z)
• PGLP: Customizable and Rigorous Location Privacy through Policy Graph [68.3736286350014]
  We propose a new location privacy notion called PGLP, which provides a rich interface to release private locations with customizable and rigorous privacy guarantee. Specifically, we formalize a user's location privacy requirements using a textitlocation policy graph, which is expressive and customizable. Third, we design a private location trace release framework that pipelines the detection of location exposure, policy graph repair, and private trajectory release with customizable and rigorous location privacy.
  arXiv  Detail & Related papers  (2020-05-04T04:25:59Z)

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.