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Found 8 papers, 2 papers with code
SecFL: Confidential Federated Learning using TEEs
Second, malicious clients can collude with each other to steal data, models from regular clients or corrupt the global training model.
Perun: Secure Multi-Stakeholder Machine Learning Framework with GPU Support
To address this problem, we designed and implemented Perun, a framework for confidential multi-stakeholder machine learning that allows users to make a trade-off between security and performance.
secureTF: A Secure TensorFlow Framework
To tackle this challenge, we designed secureTF, a distributed secure machine learning framework based on Tensorflow for the untrusted cloud infrastructure.
TEEMon: A continuous performance monitoring framework for TEEs
Currently, only a limited number of performance measurement tools for TEE-based applications exist and none offer performance monitoring and analysis during runtime.
Cryptography and Security Distributed, Parallel, and Cluster Computing Performance C.4
SpecFuzz: Bringing Spectre-type vulnerabilities to the surface
SpecFuzz is the first tool that enables dynamic testing for speculative execution vulnerabilities (e. g., Spectre).
Cryptography and Security
TensorSCONE: A Secure TensorFlow Framework using Intel SGX
This imposes significant security risks since modern online services rely on cloud computing to store and process the sensitive data.
Grand Challenge: Real-time Destination and ETA Prediction for Maritime Traffic
The challenge asks to provide a prediction for (i) a destination and the (ii) arrival time of ships in a streaming-fashion using Geo-spatial data in the maritime context.
Intel MPX Explained: An Empirical Study of Intel MPX and Software-based Bounds Checking Approaches
Memory-safety violations are a prevalent cause of both reliability and security vulnerabilities in systems software written in unsafe languages like C/C++.
Cryptography and Security
