Secure Network Function Computation for General Target and Security Functions

Secure network function computation is a critical research direction in network coding, which aims to ensure that the target function is correctly computed at the sink node while preventing the wiretapper from obtaining any information about the security function. In this paper, we focus on the...

RUSTSEC-2025-0115 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The tandem crates in https://github.com/sine-fdn are no longer maintained by the SINE Foundation. The repository has been archived.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...

tandem_garble_interop 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The tandem crates in https://github.com/sine-fdn are no longer maintained by the SINE Foundation. The repository has been archived.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...

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The tandem crates in https://github.com/sine-fdn are no longer maintained by the SINE Foundation. The repository has been archived.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...

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The tandem crates in https://github.com/sine-fdn are no longer maintained by the SINE Foundation. The repository has been archived.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...

Ransomware Negotiation: Dynamics and Privacy-Preserving Mechanism Design

Ransomware attacks have become a pervasive and costly form of cybercrime, causing tens of millions of dollars in losses as organizations increasingly pay ransoms to mitigate operational disruptions and financial risks. While prior research has largely focused on proactive defenses, the...

SecFwT: Efficient Privacy-Preserving Fine-Tuning of Large Language Models Using Forward-Only Passes

Large language models LLMs have transformed numerous fields, yet their adaptation to specialized tasks in privacy-sensitive domains, such as healthcare and finance, is constrained by the scarcity of accessible training data due to stringent privacy requirements. Secure multi-party computation...

Synopsis: Secure and Private Trend Inference from Encrypted Semantic Embeddings

WhatsApp and many other commonly used communication platforms guarantee end-to-end encryption E2EE, which requires that service providers lack the cryptographic keys to read communications on their own platforms. WhatsApp's privacy-preserving design makes it difficult to study important phenomena...

Privacy-Preserving Inconsistency Measurement

We investigate a new form of privacy-preserving inconsistency measurement for multi-party communication. Intuitively, for two knowledge bases KA, KB of two agents A, B, our results allow to quantitatively assess the degree of inconsistency for KA U KB without having to reveal the actual contents ...

Towards a DSL for Hybrid Secure Computation

Fully homomorphic encryption FHE and trusted execution environments TEE are two approaches to provide confidentiality during data processing. Each approach has its own strengths and weaknesses. In certain scenarios, computations can be carried out in a hybrid environment, using both FHE and TEE...

AIs as Trusted Third Parties

This is a truly fascinating paper: "Trusted Machine Learning Models Unlock Private Inference for Problems Currently Infeasible with Cryptography." The basic idea is that AIs can act as trusted third parties: Abstract: We often interact with untrusted parties. Prioritization of privacy can limit t...