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Evidence record 13727 · automatically gathered

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption

In the past decade, we have witnessed an exponential growth of deep learning models, platforms, and applications. While existing DL applications and Machine Learning as a service (MLaaS) frameworks assume fully trusted models, the need for privacy-preserving DNN evaluation arises. In a secure multi-party computation scenario, both the model and the data are considered proprietary, i.e., the model owner does not want to reveal the highly valuable DL model to the user, while the user does not wish

Record details

Published: 24 July 2026
Source: arXiv cs.CR (AI security)
Category: Research
Topics: Privacy
Retrieved: 27 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: Privacy

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Shared topic: Privacy

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Shared topic: Privacy

Unsupervised Consensus-Based Anomaly Detection for Spatiotemporal Malaria Incidence in Ghana

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arXiv cs.AI · 23 July 2026

Shared topic: Privacy

GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes

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Shared topic: Privacy

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How to cite this record

ethics.ai (24 July 2026), “PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption,” evidence record 13727, https://ethics.ai/record/13727 (originally published by arXiv cs.CR (AI security)).

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