This is a reading list for those interested in studying AI in mobile apps.
Privacy:
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Automated Analysis of Privacy Requirements for Mobile Apps, AAAI, 2016
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Natural Language Processing for Mobile App Privacy Compliance, ceur-ws, 2019
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MAPS: Scaling Privacy Compliance Analysis to a Million Apps, Sciendo, 2019
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Polisis: Automated Analysis and Presentation of Privacy Policies Using Deep Learning, Usenix Security, 2018
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50 Ways to Leak Your Data: An Exploration of Apps’ Circumvention of the Android Permissions System, USENIX Security, 2019
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We Value Your Privacy ... Now Take Some Cookies: Measuring the GDPR's Impact on Web Privacy, NDSS, 2019
DL Characterization
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A First Look at Deep Learning Apps on Smartphones , [WWW'19]
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NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications, ECCV, 2018
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Latency and Throughput Characterization of Convolutional Neural Networks for Mobile Computer Vision, MMSys, 2018
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Machine Learning at Facebook:Understanding Inference at the Edge, HPCA, 2019
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IoTRiskAnalyzer: A Probabilistic Model Checking Based Framework for Formal Risk Analytics of the Internet of Things, IEEE Access, 2017
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LEMNA: Explaining Deep Learning based Security Applications, IEEE Access, 2018
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Adversarial Robustness vs. Model Compression, or Both, ICCV, 2019
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Characterizing the Deep Neural Networks Inference Performance of Mobile Applications,arXiv, 2019
Surveys:
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A Survey of Machine and Deep Learning Methods for Internet of Things (IoT), Security arXiv, 2017
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A Survey of Model Compression and Acceleration for Deep Neural Networks, arXiv, 2017
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A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security, arXiv, 2017
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Adversarial Examples: Attacks and Defenses for Deep Learning, arXiv, 2017
Adversarial Examples Reading List: