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Showing 1 to 5 of 5 for “"Secure Machine Learning"”.
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Secure Machine Learning Based RF Signal Classification for Wireless Systems
… are, in general, vulnerable to adversarial machine learning (AML) attacks. In one type of AML attack, the adversary trains a surrogate classifier (called the {\em attacker's classifier}) to produce intelligently crafted low-power ``perturbations'' that degrade the accuracy of the targeted …
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Towards Secure Machine Learning Acceleration: Threats and Defenses Across Algorithms, Architecture, and Circuits
… for its parameters. Second, this thesis proposes SecureLoop, a design space exploration framework for secure DNN accelerators that support a trusted execution environment (TEE). Cryptographic operations are tightly coupled with the data movement pattern in secure DNN accelerators, complicating the …
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Using Public and Private Blockchains for Secure Data Sharing and Analytics
… on private bids) and make sure those results are secure. In another use case, we may need to run Machine Learning(ML) algorithms to generate ML models on the shared data. In each of these use cases, along with the compliance of regulatory standards, we need to ensure that data privacy is preserved …
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Human factors in secure and non-abusive machine learning systems
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms