PrivacyCredit is a machine learning method that combines traditional and alternative data for credit risk prediction while satisfying privacy-preserving, model-confidential, and lossless properties.
Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption
7 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Consider two data providers, each maintaining private records of different feature sets about common entities. They aim to learn a linear model jointly in a federated setting, namely, data is local and a shared model is trained from locally computed updates. In contrast with most work on distributed learning, in this scenario (i) data is split vertically, i.e. by features, (ii) only one data provider knows the target variable and (iii) entities are not linked across the data providers. Hence, to the challenge of private learning, we add the potentially negative consequences of mistakes in entity resolution. Our contribution is twofold. First, we describe a three-party end-to-end solution in two phases ---privacy-preserving entity resolution and federated logistic regression over messages encrypted with an additively homomorphic scheme---, secure against a honest-but-curious adversary. The system allows learning without either exposing data in the clear or sharing which entities the data providers have in common. Our implementation is as accurate as a naive non-private solution that brings all data in one place, and scales to problems with millions of entities with hundreds of features. Second, we provide what is to our knowledge the first formal analysis of the impact of entity resolution's mistakes on learning, with results on how optimal classifiers, empirical losses, margins and generalisation abilities are affected. Our results bring a clear and strong support for federated learning: under reasonable assumptions on the number and magnitude of entity resolution's mistakes, it can be extremely beneficial to carry out federated learning in the setting where each peer's data provides a significant uplift to the other.
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2026 7representative citing papers
Appraisal is a circuit-PSI system for privacy-preserving screening in record linkage that adds an Oblivious Attribute/Feature Alignment protocol to support approximate matching, cutting communication 14x and scaling to 850x more records than prior PPRS while being 165x faster than SOTA PPRL.
PPHH-VFL splits the model head into a plaintext public part secured by adversarial training and a small MPC private part, yielding up to 6 orders of magnitude faster inference than end-to-end MPC on models up to 86M parameters.
X-NegoBox is a proposed explainable framework that negotiates privacy budgets for energy data exchange using trust, sensitivity, and purpose factors, with experiments claiming reduced leakage and higher acceptance rates.
FedProxy replaces weak adapters with a proxy SLM for federated LLM fine-tuning, outperforming prior methods and approaching centralized performance via compression, heterogeneity-aware aggregation, and training-free fusion.
The paper surveys split and aggregation learning for foundation models in 6G networks to improve efficiency, resource use, and data privacy in distributed AI.
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