Privacy attacks against deep learning models and their countermeasures

[1]  Mohammad Al-Rubaie,et al.  Privacy-Preserving Machine Learning: Threats and Solutions , 2018, IEEE Security & Privacy.

[2]  Zibin Zheng,et al.  Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure Smart Grids , 2018, IEEE Transactions on Industrial Informatics.

[3]  Anqi Zhang,et al.  LocMIA: Membership Inference Attacks Against Aggregated Location Data , 2020, IEEE Internet of Things Journal.

[4]  Fagen Li,et al.  A cloud data deduplication scheme based on certificateless proxy re-encryption , 2020, J. Syst. Archit..

[5]  Wei Zhang,et al.  Differential Privacy Preservation in Deep Learning: Challenges, Opportunities and Solutions , 2019, IEEE Access.

[6]  Erchin Serpedin,et al.  PPETD: Privacy-Preserving Electricity Theft Detection Scheme With Load Monitoring and Billing for AMI Networks , 2019, IEEE Access.

[7]  Qi Shi,et al.  A Deep Learning Approach to Network Intrusion Detection , 2018, IEEE Transactions on Emerging Topics in Computational Intelligence.

[8]  Elisa Bertino,et al.  Differentially Private K-Means Clustering and a Hybrid Approach to Private Optimization , 2017, ACM Trans. Priv. Secur..

[9]  Jing Chen,et al.  Rethinking compact abating probability modeling for open set recognition problem in Cyber-physical systems , 2019, J. Syst. Archit..

[10]  Arjen van Ooyen,et al.  Improving the convergence of the back-propagation algorithm , 1992, Neural Networks.

[11]  Ximeng Liu,et al.  Lightweight Privacy-Preserving Medical Diagnosis in Edge Computing , 2022, IEEE Transactions on Services Computing.

[12]  Xin He,et al.  Attacking Vision-based Perception in End-to-End Autonomous Driving Models , 2019, J. Syst. Archit..

[13]  Yin Yang,et al.  Functional Mechanism: Regression Analysis under Differential Privacy , 2012, Proc. VLDB Endow..

[14]  Aaron Roth,et al.  The Algorithmic Foundations of Differential Privacy , 2014, Found. Trends Theor. Comput. Sci..

[15]  David J. Wu,et al.  Function-Hiding Inner Product Encryption is Practical , 2018, IACR Cryptol. ePrint Arch..

[16]  Li Liu,et al.  A local external coupling matrix solution and dynamic processing in medical cyber-physical cloud systems , 2020, J. Syst. Archit..

[17]  Carl A. Gunter,et al.  Plausible Deniability for Privacy-Preserving Data Synthesis , 2017, Proc. VLDB Endow..

[18]  Zhiwei Steven Wu,et al.  Privacy-Preserving Generative Deep Neural Networks Support Clinical Data Sharing , 2017, bioRxiv.

[19]  Anil K. Jain,et al.  Fingerprint Reconstruction: From Minutiae to Phase , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Anand D. Sarwate,et al.  Differentially Private Empirical Risk Minimization , 2009, J. Mach. Learn. Res..

[21]  Robert Laganière,et al.  Membership Inference Attack against Differentially Private Deep Learning Model , 2018, Trans. Data Priv..

[22]  Giovanni Felici,et al.  Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers , 2013, Int. J. Secur. Networks.

[23]  Fabio Roli,et al.  Evasion Attacks against Machine Learning at Test Time , 2013, ECML/PKDD.

[24]  Ching-Te Chiu,et al.  Multi-teacher knowledge distillation for compressed video action recognition based on deep learning , 2020, J. Syst. Archit..

[25]  Wei Cai,et al.  A Survey on Security Threats and Defensive Techniques of Machine Learning: A Data Driven View , 2018, IEEE Access.

[26]  Gabor Karsai,et al.  URMILA: Dynamically trading-off fog and edge resources for performance and mobility-aware IoT services , 2020, J. Syst. Archit..

[27]  Frank McSherry.  Privacy integrated queries , 2010, Commun. ACM.

[28]  J. Morris Chang,et al.  Reconstruction Attacks Against Mobile-Based Continuous Authentication Systems in the Cloud , 2016, IEEE Transactions on Information Forensics and Security.