AI ethics evidence index Search
Source-linked daily record
Evidence record 5555 · automatically gathered

Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers

Federated Learning (FL) enables collaborative model training among multiple parties without centralizing raw data. There are two main paradigms in FL: Horizontal FL (HFL), where all participants share the same feature space but hold different samples, and Vertical FL (VFL), where parties possess complementary features for the same set of samples. A prerequisite for VFL training is privacy-preserving entity alignment (PPEA), which establishes a common index of samples across parties (alignment) w

Record details

Published: 21 April 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Privacy · Transparency
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Safety & alignment · Shared topic: Privacy

Measuring the Depth of LLM Unlearning via Activation Patching

arXiv · 23 May 2026

Shared topic: Safety & alignment · Shared topic: Privacy

VisualLeakBench: Auditing the Fragility of Large Vision-Language Models against PII Leakage and Social Engineering

arXiv · 11 March 2026

Shared topic: Safety & alignment · Shared topic: Privacy

Trustworthy Machine Learning through the Lens of Combinatorial Optimization: Survey and Research Perspectives

arXiv fairness query · 8 July 2026

Shared topic: Safety & alignment · Shared topic: Privacy

CIDER: A Dataset of Contextual Disclosure Boundaries for Privacy Preference Alignment

arXiv · 10 August 2026

Shared topic: Safety & alignment · Shared topic: Transparency

Taming Actor-Observer Asymmetry in Agents via Dialectical Alignment

arXiv · 21 April 2026

Shared topic: Safety & alignment · Shared topic: Privacy

Reinforcing privacy reasoning in LLMs via normative simulacra from fiction

arXiv · 21 April 2026

How to cite this record

ethics.ai (21 April 2026), “Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers,” evidence record 5555, https://ethics.ai/record/5555 (originally published by arXiv).

JSON

Use and limitations

This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.