Secure Transfer Learning for Machine Fault Diagnosis Under Different Operating Conditions

[1]  Craig Gentry,et al.  (Leveled) Fully Homomorphic Encryption without Bootstrapping , 2014, ACM Trans. Comput. Theory.

[2]  Vinod Vaikuntanathan,et al.  Fully Homomorphic Encryption from Ring-LWE and Security for Key Dependent Messages , 2011, CRYPTO.

[3]  Yoshua Bengio,et al.  How transferable are features in deep neural networks? , 2014, NIPS.

[4]  Andrew Chi-Chih Yao,et al.  How to generate and exchange secrets , 1986, 27th Annual Symposium on Foundations of Computer Science (sfcs 1986).

[5]  Pascal Paillier,et al.  Fast Homomorphic Evaluation of Deep Discretized Neural Networks , 2018, IACR Cryptol. ePrint Arch..

[6]  Silvio Micali,et al.  A Completeness Theorem for Protocols with Honest Majority , 1987, STOC 1987.

[7]  Frederik Vercauteren,et al.  Somewhat Practical Fully Homomorphic Encryption , 2012, IACR Cryptol. ePrint Arch..

[8]  Yao Lu,et al.  Oblivious Neural Network Predictions via MiniONN Transformations , 2017, IACR Cryptol. ePrint Arch..

[9]  Razvan Pascanu,et al.  Sim-to-Real Robot Learning from Pixels with Progressive Nets , 2016, CoRL.

[10]  Meng Chen,et al.  Intelligent Fault Diagnosis of Rotary Machinery Based on Unsupervised Multiscale Representation Learning , 2017 .

[11]  Raluca Ada Popa,et al.  Delphi: A Cryptographic Inference System for Neural Networks , 2020, IACR Cryptol. ePrint Arch..

[12]  Jung Hee Cheon,et al.  Homomorphic Encryption for Arithmetic of Approximate Numbers , 2017, ASIACRYPT.

[13]  J. Concato,et al.  A simulation study of the number of events per variable in logistic regression analysis. , 1996, Journal of clinical epidemiology.

[14]  Michael Naehrig,et al.  CryptoNets: applying neural networks to encrypted data with high throughput and accuracy , 2016, ICML 2016.

[15]  Shai Halevi,et al.  Algorithms in HElib , 2014, CRYPTO.

[16]  Farinaz Koushanfar,et al.  XONN: XNOR-based Oblivious Deep Neural Network Inference , 2019, IACR Cryptol. ePrint Arch..

[17]  J. M. Tarela,et al.  Approximation of sigmoid function and the derivative for hardware implementation of artificial neurons , 2004 .

[18]  Craig Gentry,et al.  Fully homomorphic encryption using ideal lattices , 2009, STOC '09.

[19]  Frederik Vercauteren,et al.  Fully homomorphic SIMD operations , 2012, Designs, Codes and Cryptography.

[20]  Xiaoqian Jiang,et al.  Secure Outsourced Matrix Computation and Application to Neural Networks , 2018, CCS.

[21]  Michalis K. Titsias,et al.  One-vs-Each Approximation to Softmax for Scalable Estimation of Probabilities , 2016, NIPS.

[22]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[23]  Jung Hee Cheon,et al.  Logistic regression model training based on the approximate homomorphic encryption , 2018, BMC Medical Genomics.

[24]  William E. Burr,et al.  Recommendation for Key Management, Part 1: General , 2005 .

[25]  Payman Mohassel,et al.  SecureML: A System for Scalable Privacy-Preserving Machine Learning , 2017, 2017 IEEE Symposium on Security and Privacy (SP).

[26]  Silvio Micali,et al.  How to play ANY mental game , 1987, STOC.

[27]  Jianfei Yu,et al.  Learning Sentence Embeddings with Auxiliary Tasks for Cross-Domain Sentiment Classification , 2016, EMNLP.

[28]  Gerald Penn,et al.  Efficient Evaluation of Activation Functions over Encrypted Data , 2019, 2019 IEEE Security and Privacy Workshops (SPW).

[29]  M. Vlcek CHEBYSHEV POLYNOMIAL APPROXIMATION FOR ACTIVATION SIGMOID FUNCTION , 2012 .

[30]  Anantha Chandrakasan,et al.  Gazelle: A Low Latency Framework for Secure Neural Network Inference , 2018, IACR Cryptol. ePrint Arch..

[31]  Ronald L. Rivest,et al.  ON DATA BANKS AND PRIVACY HOMOMORPHISMS , 1978 .

[32]  Xiaoqian Jiang,et al.  Secure Logistic Regression Based on Homomorphic Encryption: Design and Evaluation , 2018, IACR Cryptol. ePrint Arch..