{"ID":2895252,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.09678","arxiv_id":"2507.09678","title":"Conformal Prediction for Privacy-Preserving Machine Learning","abstract":"We investigate the integration of Conformal Prediction (CP) with supervised learning on deterministically encrypted data, aiming to bridge the gap between rigorous uncertainty quantification and privacy-preserving machine learning. Using AES-encrypted variants of the MNIST dataset, we demonstrate that CP methods remain effective even when applied directly in the encrypted domain, owing to the preservation of data exchangeability under fixed-key encryption. We test traditional $p$-value-based against $e$-value-based conformal predictors. Our empirical evaluation reveals that models trained on deterministically encrypted data retain the ability to extract meaningful structure, achieving 36.88\\% test accuracy -- significantly above random guessing (9.56\\%) observed with per-instance encryption. Moreover, $e$-value-based CP achieves predictive set coverage of over 60\\% with 4.3 loss-threshold calibration, correctly capturing the true label in 4888 out of 5000 test cases. In contrast, the $p$-value-based CP yields smaller predictive sets but with reduced coverage accuracy. These findings highlight both the promise and limitations of CP in encrypted data settings and underscore critical trade-offs between prediction set compactness and reliability. %Our work sets a foundation for principled uncertainty quantification in secure, privacy-aware learning systems.","short_abstract":"We investigate the integration of Conformal Prediction (CP) with supervised learning on deterministically encrypted data, aiming to bridge the gap between rigorous uncertainty quantification and privacy-preserving machine learning. Using AES-encrypted variants of the MNIST dataset, we demonstrate that CP methods remain...","url_abs":"https://arxiv.org/abs/2507.09678","url_pdf":"https://arxiv.org/pdf/2507.09678v1","authors":"[\"Alexander David Balinsky\",\"Dominik Krzeminski\",\"Alexander Balinsky\"]","published":"2025-07-13T15:29:14Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"math.ST\"]","methods":"[]","has_code":false}