Towards integration of privacy enhancing technologies in explainable artificial intelligence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Explainable artificial intelligence (XAI) plays a crucial role in mitigating the risks associated with the non-transparency of black-box artificial intelligence (AI) systems. However, despite its advantages, XAI methods have been shown to expose the privacy of individuals whose data are used to train or query the underlying models. Prior research has demonstrated privacy attacks that exploit explanations to infer sensitive personal information of individuals. At present, there is a lack of effective defenses against such privacy attacks targeting explanations, particularly when vulnerable XAI techniques are deployed in production environments or used in machine learning as a service systems. To address this gap, this study investigates the use of privacy enhancing technologies (PETs) as a defense mechanism against attribute inference attacks on explanations generated by feature-based XAI methods. We empirically evaluate three types of PETs, i.e., synthetic training data, differentially private training and noise addition, across two categories of feature-based XAI. Our findings reveal varying levels of effectiveness among the mitigation strategies, as well as trade-offs between privacy, utility and system performance. In the best scenario, integrating PETs into the explanation process reduced attack success by 49.47% while preserving model utility and explanation quality. Based on our evaluation, we propose strategies for effectively integrating PETs into XAI to maximize privacy protection and minimize the risk of sensitive information leakage.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.016 | 0.015 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it