26 research outputs found

    Security framework for the semiconductor supply chain environment

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    This paper proposes a security framework for secure data communications across the partners in the Semiconductor Supply Chain Environment. The security mechanisms of the proposed framework will be based on the SSL/TLS and OAuth 2.0 protocols, which are two standard security protocols. However, both protocols are vulnerable to a number of attacks, and thus more sophisticated security mechanisms based on these protocols should be designed and implemented in order to address the specific security challenges of the Semiconductor Supply Chain in a more effective and efficient manner

    Through the Looking-Glass: Benchmarking Secure Multi-Party Computation Comparisons for ReLU\u27s

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    Comparisons or Inequality Tests are an essential building block of Rectified Linear Unit functions (ReLU\u27s), ever more present in Machine Learning, specifically in Neural Networks. Motivated by the increasing interest in privacy-preserving Artificial Intelligence, we explore the current state of the art of privacy preserving comparisons over Multi-Party Computation (MPC). We then introduce constant round variations and combinations, which are compatible with customary fixed point arithmetic over MPC. Our main focus is implementation and benchmarking; hence, we showcase our contributions via an open source library, compatible with current MPC software tools. Furthermore, we include a comprehensive comparative analysis on various adversarial settings. Our results improve running times in practical scenarios. Finally, we offer conclusions about the viability of these protocols when adopted for privacy-preserving Machine Learning
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