Private Counterfactual Retrieval

Nomeir, Mohamed; Dissanayake, Pasan; Meel, Shreya; Dutta, Sanghamitra; Ulukus, Sennur

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https://hdl.handle.net/2142/130285 Copy

Description

Title

Private Counterfactual Retrieval

Author(s)

• Nomeir, Mohamed
• Dissanayake, Pasan
• Meel, Shreya
• Dutta, Sanghamitra
• Ulukus, Sennur

Issue Date

2025-09-17

Keyword(s)

• Counterfactual retrieval
• Information theoretic privacy
• User preferences and actionability

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 fulfilling 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 user’s privacy when retrieving counterfactual explanations. We present a scheme which retrieves the 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. Finally, we present numerical results to support our theoretical findings, and compare the database leakage of the proposed schemes.

Publisher

Allerton Conference on Communication, Control, and Computing

Series/Report Name or Number

2025 61st Allerton Conference on Communication, Control, and Computing Proceedings

ISSN

2836-4503

Type of Resource

Text

Genre of Resource

Conference Paper/Presentation

Language

eng

Handle URL

https://hdl.handle.net/2142/130285

Copyright and License Information

Copyright 2025 owned by the authors.

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