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nGraph-HE2: A High-Throughput Framework for Neural Network Inference on Encrypted Data
Privacy-Preserving Machine Learning Deep Learning Graph Compilers
2019/8/21
In previous work, Boemer et al. introduced nGraph-HE, an extension to the Intel nGraph deep learning (DL) compiler, that en- ables data scientists to deploy models with popular frameworks such as Tens...
Neural Network Model Assessment for Side-Channel Analysis
Side-Channel Analysis Neural Networks Model Assessment
2019/6/19
Leakage assessment of cryptographic implementations with side-channel analysis relies on two important assumptions: leakage model and the number of side-channel traces. In the context of profiled side...
Efficient Multi-Key Homomorphic Encryption with Packed Ciphertexts with Application to Oblivious Neural Network Inference
multi-key homomorphic encryption packed ciphertext ring learning with errors
2019/5/21
Homomorphic Encryption (HE) is a cryptosystem which supports computation on encrypted data. López-Alt et al. (STOC 2012) proposed a generalized notion of HE, called Multi-Key Homomorphic Encryption (M...
Experimental Evaluation of Deep Neural Network Resistance Against Fault Injection Attacks
fault attack neural network deep learning
2019/5/13
Deep learning is becoming a basis of decision making systems in many application domains, such as autonomous vehicles, health systems, etc., where the risk of misclassification can lead to serious con...
Deep Neural Network Attribution Methods for Leakage Analysis and Symmetric Key Recovery
Side-Channel Attacks Deep Learning Machine Learning
2019/2/26
Deep Neural Networks (DNNs) have recently received significant attention in the side-channel community due to their state-of-the-art performance in security testing of embedded systems. However, resea...
XONN: XNOR-based Oblivious Deep Neural Network Inference
Privacy-Preserving Machine Learning Deep Learning Oblivious Inference
2019/2/25
Advancements in deep learning enable cloud servers to provide inference-as-a-service for clients. In this scenario, clients send their raw data to the server to run the deep learning model and send ba...
CSI Neural Network: Using Side-channels to Recover Your Artificial Neural Network Information
Side-channel Analysis Artificial Neural Networks Power
2018/5/28
Machine learning has become mainstream across industries. In this work we pose the following question: Is it possible to reverse engineer a neural network by using only side-channel information? We an...
SecureNN: Efficient and Private Neural Network Training
secure computation neural network training information-theoretic security
2018/5/15
Neural Networks (NN) provide a powerful method for machine learning training and prediction. For effective training, it is often desirable for multiple parties to combine their data -- however, doing ...
GAZELLE: A Low Latency Framework for Secure Neural Network Inference
homomorphic encryption two-party secure computation convolutional neural networks
2018/1/19
The growing popularity of cloud-based machine learning raises a natural question about the privacy guarantees that can be provided in such a setting. Our work tackles this problem in the context where...
Private Collaborative Neural Network Learning
deep learning neural networks differential privacy
2017/8/10
Machine learning algorithms, such as neural networks, create better predictive models when having access to larger datasets. In many domains, such as medicine and finance, each institute has onl...
Oblivious Neural Network Predictions via MiniONN transformations
privacy machine learning neural network predictions
2017/5/25
Machine learning models hosted in a cloud service are increasingly popular but risk privacy: clients sending prediction requests to the service need to disclose potentially sensitive information. In t...
Neural Networks (NN) are today increasingly used in Machine Learning where they have become deeper and deeper to accurately model or classify high-level abstractions of data. Their development however...
