Private Counterfactual Retrieval With Immutable Features

• URL: http://arxiv.org/abs/2411.10429v1
• Date: Fri, 15 Nov 2024 18:50:53 GMT
• Title: Private Counterfactual Retrieval With Immutable Features
• Authors: Shreya Meel, Pasan Dissanayake, Mohamed Nomeir, Sanghamitra Dutta, Sennur Ulukus,
• Abstract summary: 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.
• Score: 34.11302393278422
• License:
• Abstract: 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 while enforcing that certain features of the input sample cannot be changed, i.e., they are \emph{immutable}. An applicant (user) whose feature vector is rejected by a machine learning model wants to retrieve the sample closest to them in the database without altering a private subset of their features, which constitutes the immutable set. While doing this, the user should keep their feature vector, immutable set and the resulting counterfactual index information-theoretically private from the institution. We refer to this as immutable private counterfactual retrieval (I-PCR) problem which generalizes PCR to a more practical setting. In this paper, we propose two I-PCR schemes by leveraging techniques from private information retrieval (PIR) and characterize their communication costs. Further, we quantify the information that the user learns about the database and compare it for the proposed schemes.

Related papers

• Private Counterfactual Retrieval [34.11302393278422]
  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.
  arXiv  Detail & Related papers  (2024-10-17T17:45:07Z)
• Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach [49.63614966954833]
  Federated Collaborative Filtering (FedCF) is an emerging field focused on developing a new recommendation framework with preserving privacy. This paper proposes a novel personalized FedCF method by preserving users' personalized information into a latent variable and a neural model simultaneously. To effectively train the proposed framework, we model the problem as a specialized Variational AutoEncoder (VAE) task by integrating user interaction vector reconstruction with missing value prediction.
  arXiv  Detail & Related papers  (2024-08-16T05:49:14Z)
• Wiki-En-ASR-Adapt: Large-scale synthetic dataset for English ASR Customization [66.22007368434633]
  We present a first large-scale public synthetic dataset for contextual spellchecking customization of automatic speech recognition (ASR) The proposed approach allows creating millions of realistic examples of corrupted ASR hypotheses and simulate non-trivial biasing lists for the customization task. We report experiments with training an open-source customization model on the proposed dataset and show that the injection of hard negative biasing phrases decreases WER and the number of false alarms.
  arXiv  Detail & Related papers  (2023-09-29T14:18:59Z)
• Generalized PTR: User-Friendly Recipes for Data-Adaptive Algorithms with Differential Privacy [16.374676154278614]
  ''Propose-Test-Release'' (PTR) is a classic recipe for designing differentially private (DP) algorithms. We extend PTR to a more general setting by privately testing data-dependent privacy losses rather than local sensitivity.
  arXiv  Detail & Related papers  (2022-12-31T22:22:53Z)
• Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries [53.222218035435006]
  We use adversarial tools to optimize for queries that are discriminative and diverse. Our improvements achieve significantly more accurate membership inference than existing methods.
  arXiv  Detail & Related papers  (2022-10-19T17:46:50Z)
• The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm [154.47590401735323]
  Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems. This paper focuses on a challenging scenario where a user has multiple categories of interests. We propose a novel method called textitDiversity-Promoting Collaborative Metric Learning (DPCML)
  arXiv  Detail & Related papers  (2022-09-30T08:02:18Z)
• Multi-class Classifier based Failure Prediction with Artificial and Anonymous Training for Data Privacy [0.0]
  A neural network based multi-class classifier is developed for failure prediction. The proposed mechanism completely decouples the data set used for training process from the actual data which is kept private. Results show high accuracy in failure prediction under different parameter configurations.
  arXiv  Detail & Related papers  (2022-09-06T07:53:33Z)
• Determination of class-specific variables in nonparametric multiple-class classification [0.0]
  We propose a probability-based nonparametric multiple-class classification method, and integrate it with the ability of identifying high impact variables for individual class. We report the properties of the proposed method, and use both synthesized and real data sets to illustrate its properties under different classification situations.
  arXiv  Detail & Related papers  (2022-05-07T10:08:58Z)
• 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)
• Control Variates for Slate Off-Policy Evaluation [112.35528337130118]
  We study the problem of off-policy evaluation from batched contextual bandit data with multidimensional actions. We obtain new estimators with risk improvement guarantees over both the PI and self-normalized PI estimators.
  arXiv  Detail & Related papers  (2021-06-15T06:59:53Z)

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.