Security of neuromorphic computing: Thwarting learning attacks using memristor's obsolescence effect
© 2016 ACM. Neuromorphic architectures are widely used in many applications for advanced data processing, and often implements proprietary algorithms. In this work, we prevent an attacker with physical access from learning the proprietary algorithm implemented by the neuromorphic hardware. For this purpose, we leverage the obsolescence effect in memristors to judiciously reduce the accuracy of outputs for any unauthorized user. For a legitimate user, we regulate the obsolescence effect, thereby controlling the accuracy of outputs. We also analyze the security vs. cost trade-offs for different applications. Our methodology is compatible with mainstream classification applications, memristor devices, and security and performance constraints.
Yang, C; Liu, B; Li, H; Chen, Y; Wen, W; Barnell, M; Wu, Q; Rajendran, J
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International Standard Book Number 13 (ISBN-13)
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