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[电气工程与系统科学] 隐私保护分类器识别来自 WiFi网络不完善数据 [推广有奖]

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何人来此 在职认证  发表于 2022-4-6 12:50:00 来自手机 |AI写论文

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摘要翻译:
本文证明了仅基于便携设备与WiFi接入点之间的连接日志,在保护用户隐私的前提下,准确识别特定的人类移动共享模式是可行的。我们从Polytechnology Montreal的Eduroam WiFi网络收集数据,省略了设备跟踪或物理层数据。我们选择检测的行为是与学术课程结束相关的运动,以及与课间小休息时间相关的模式。在我们的实验中,自我施加了严格的条件。数据是已知的有误差噪声,并易受信息丢失。没有采取任何对策来缓解这些问题。数据预处理包括用于按时间间隔聚合数据的基本统计信息。我们在识别类间和类末的中断行为模式时,分别获得了93.7%和83.3%的准确率(通过袋装树)。
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英文标题:
《Privacy-preserving classifiers recognize shared mobility behaviours from
  WiFi network imperfect data》
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作者:
Orestes Manzanilla-Salazar (1) and Brunilde Sans\`o (1) ((1)
  Polytechnique Montr\'eal)
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最新提交年份:
2018
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分类信息:

一级分类:Electrical Engineering and Systems Science        电气工程与系统科学
二级分类:Signal Processing        信号处理
分类描述:Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用,包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、(生物)医学、图像、视频和多模态自然和人为信号,包括通信信号和数据。感兴趣的主题包括:统计信号处理、谱估计和系统辨识;滤波器设计;自适应滤波/随机学习;(压缩)采样、传感和变换域方法,包括快速算法;用于机器学习的信号处理和用于信号处理应用的机器学习;网络与图形信号处理;信号处理中的凸和非凸优化方法;雷达、声纳和传感器阵列波束形成和测向;通信信号处理;低功耗、多核、片上系统信号处理;信息物理系统的传感、通信、分析和优化,如电网和物联网。
--
一级分类:Computer Science        计算机科学
二级分类:Cryptography and Security        密码学与安全
分类描述:Covers all areas of cryptography and security including authentication, public key cryptosytems, proof-carrying code, etc. Roughly includes material in ACM Subject Classes D.4.6 and E.3.
涵盖密码学和安全的所有领域,包括认证、公钥密码系统、携带证明的代码等。大致包括ACM主题课程D.4.6和E.3中的材料。
--
一级分类:Computer Science        计算机科学
二级分类:Machine Learning        机器学习
分类描述:Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文(有监督的,无监督的,强化学习,强盗问题,等等),包括健壮性,解释性,公平性和方法论。对于机器学习方法的应用,CS.LG也是一个合适的主要类别。
--
一级分类:Statistics        统计学
二级分类:Applications        应用程序
分类描述:Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学,教育学,流行病学,工程学,环境科学,医学,物理科学,质量控制,社会科学
--

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英文摘要:
  This paper proves the concept that it is feasible to accurately recognize specific human mobility shared patterns, based solely on the connection logs between portable devices and WiFi Access Points (APs), while preserving user's privacy. We gathered data from the Eduroam WiFi network of Polytechnique Montreal, making omission of device tracking or physical layer data. The behaviors we chose to detect were the movements associated to the end of an academic class, and the patterns related to the small break periods between classes. Stringent conditions were self-imposed in our experiments. The data is known to have errors noise, and be susceptible to information loss. No countermeasures were adopted to mitigate any of these issues. Data pre-processing consists of basic statistics that were used in aggregating the data in time intervals. We obtained accuracy values of 93.7 % and 83.3 % (via Bagged Trees) when recognizing behaviour patterns of breaks between classes and end-of-classes, respectively.
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PDF链接:
https://arxiv.org/pdf/1807.0619
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关键词:隐私保护 WiFi IFI 分类器 Applications 数据 袋装 网络 data 相关

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