tandem_http_server is unmaintained

The tandem crates in https://github.com/sine-fdn are no longer maintained by the SINE Foundation. The repository has been archived. Recommended alternative We are continuing our work on SMPC by implementing our secure multi-party computation engine Polytune...

RUSTSEC-2025-0114 tandem_http_client is unmaintained

The tandem crates in https://github.com/sine-fdn are no longer maintained by the SINE Foundation. The repository has been archived. Recommended alternative We are continuing our work on SMPC by implementing our secure multi-party computation engine Polytune...

RUSTSEC-2025-0117 tandem is unmaintained

The tandem crates in https://github.com/sine-fdn are no longer maintained by the SINE Foundation. The repository has been archived. Recommended alternative We are continuing our work on SMPC by implementing our secure multi-party computation engine Polytune...

Efficient Private Inference Based on Helper-Assisted Malicious Security Dishonest Majority MPC

Private inference based on Secure Multi-Party Computation MPC addresses data privacy risks in Machine Learning as a Service MLaaS. However, existing MPC-based private inference frameworks focuses on semi-honest or honest majority models, whose threat models are overly idealistic, while malicious...

IDCloak: a Practical Secure Multi-Party Dataset Join Framework for Vertical Privacy-Preserving Machine Learning

Vertical privacy-preserving machine learning vPPML enables multiple parties to train models on their vertically distributed datasets while keeping datasets private. In vPPML, it is critical to perform the secure dataset join, which aligns features corresponding to intersection IDs across datasets...

Private Transformer Inference in MLaaS: a Survey

Transformer models have revolutionized AI, powering applications like content generation and sentiment analysis. However, their deployment in Machine Learning as a Service MLaaS raises significant privacy concerns, primarily due to the centralized processing of sensitive user data. Private...