Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services最新文献

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DarkneTZ: towards model privacy at the edge using trusted execution environments DarkneTZ:使用可信的执行环境在边缘建立隐私模型

Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services Pub Date : 2020-04-12 DOI: 10.1145/3386901.3388946

Fan Mo, A. Shamsabadi, Kleomenis Katevas, Soteris Demetriou, Ilias Leontiadis, A. Cavallaro, H. Haddadi

{"title":"DarkneTZ: towards model privacy at the edge using trusted execution environments","authors":"Fan Mo, A. Shamsabadi, Kleomenis Katevas, Soteris Demetriou, Ilias Leontiadis, A. Cavallaro, H. Haddadi","doi":"10.1145/3386901.3388946","DOIUrl":"https://doi.org/10.1145/3386901.3388946","url":null,"abstract":"We present DarkneTZ, a framework that uses an edge device's Trusted Execution Environment (TEE) in conjunction with model partitioning to limit the attack surface against Deep Neural Networks (DNNs). Increasingly, edge devices (smartphones and consumer IoT devices) are equipped with pre-trained DNNs for a variety of applications. This trend comes with privacy risks as models can leak information about their training data through effective membership inference attacks (MIAs). We evaluate the performance of DarkneTZ, including CPU execution time, memory usage, and accurate power consumption, using two small and six large image classification models. Due to the limited memory of the edge device's TEE, we partition model layers into more sensitive layers (to be executed inside the device TEE), and a set of layers to be executed in the untrusted part of the operating system. Our results show that even if a single layer is hidden, we can provide reliable model privacy and defend against state of the art MIAs, with only 3% performance overhead. When fully utilizing the TEE, DarkneTZ provides model protections with up to 10% overhead.","PeriodicalId":345029,"journal":{"name":"Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116055865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 102

See through smoke: robust indoor mapping with low-cost mmWave radar 透过烟雾:强大的室内测绘与低成本毫米波雷达

Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services Pub Date : 2019-11-01 DOI: 10.1145/3386901.3388945

Chris Xiaoxuan Lu, Stefano Rosa, Peijun Zhao, Bing Wang, Changhao Chen, J. Stankovic, Niki Trigoni, A. Markham

{"title":"See through smoke: robust indoor mapping with low-cost mmWave radar","authors":"Chris Xiaoxuan Lu, Stefano Rosa, Peijun Zhao, Bing Wang, Changhao Chen, J. Stankovic, Niki Trigoni, A. Markham","doi":"10.1145/3386901.3388945","DOIUrl":"https://doi.org/10.1145/3386901.3388945","url":null,"abstract":"This paper presents the design, implementation and evaluation of milliMap, a single-chip millimetre wave (mmWave) radar based indoor mapping system targetted towards low-visibility environments to assist in emergency response. A unique feature of milliMap is that it only leverages a low-cost, off-the-shelf mmWave radar, but can reconstruct a dense grid map with accuracy comparable to lidar, as well as providing semantic annotations of objects on the map. milliMap makes two key technical contributions. First, it autonomously overcomes the sparsity and multi-path noise of mmWave signals by combining cross-modal supervision from a co-located lidar during training and the strong geometric priors of indoor spaces. Second, it takes the spectral response of mmWave reflections as features to robustly identify different types of objects e.g. doors, walls etc. Extensive experiments in different indoor environments show that milliMap can achieve a map reconstruction error less than 0.2m and classify key semantics with an accuracy of ~ 90%, whilst operating through dense smoke.","PeriodicalId":345029,"journal":{"name":"Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services","volume":"282 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121368505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 108

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