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Denoising and Verification Cross-Layer Ensemble Against Black-box Adversarial Attacks., , , and . CoRR, (2019)Adversarial Examples in Deep Learning: Characterization and Divergence., , , , and . CoRR, (2018)A Comparative Measurement Study of Deep Learning as a Service Framework., , , , , , and . CoRR, (2018)Towards Demystifying Membership Inference Attacks., , , , and . CoRR, (2018)Secure and Utility-Aware Data Collection with Condensed Local Differential Privacy., , , , and . CoRR, (2019)Deep Neural Network Ensembles against Deception: Ensemble Diversity, Accuracy and Robustness., , , , , , and . CoRR, (2019)Demystifying Learning Rate Polices for High Accuracy Training of Deep Neural Networks., , , , , , , , and . CoRR, (2019)Private and Truthful Aggregative Game for Large-Scale Spectrum Sharing., , , , and . CoRR, (2016)Benchmarking Deep Learning Frameworks: Design Considerations, Metrics and Beyond., , , , , and . ICDCS, page 1258-1269. IEEE Computer Society, (2018)Utility-Aware Synthesis of Differentially Private and Attack-Resilient Location Traces., , , , and . ACM Conference on Computer and Communications Security, page 196-211. ACM, (2018)