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Title: Poster : recovering the input of neural networks via single shot side-channel attacks
Authors: Batina, Lejla
Jap, Dirmanto
Bhasin, Shivam
Picek, Stjepan
Keywords: Science::Mathematics::Discrete mathematics::Cryptography
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2019
Source: Batina, L., Jap, D., Bhasin, S. & Picek, S. (2019). Poster : recovering the input of neural networks via single shot side-channel attacks. Conference on Computer and Communications Security (CCS 2019), 2657-2659.
Project: NRF2018–NCR–NCR009–0001
metadata.dc.contributor.conference: Conference on Computer and Communications Security (CCS 2019)
Abstract: The interplay between machine learning and security is becoming more prominent. New applications using machine learning also bring new security risks. Here, we show it is possible to reverse-engineer the inputs to a neural network with only a single-shot side-channel measurement assuming the attacker knows the neural network architecture being used.
ISBN: 9781450367479
DOI: 10.1145/3319535.3363280
Research Centres: Temasek Laboratories @ NTU 
Rights: © 2019 The Owner/Author(s). All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:TL Conference Papers

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