The proliferation of smart, connected, always listening devices have introduced significant privacy risks to users in a smart home environment. Beyond the notable risk of eavesdropping, intruders can adopt machine learning techniques to infer sensitive information from audio recordings on these devices, resulting in a new dimension of privacy concerns and attack variables to smart home users. Techniques such as sound masking and microphone jamming have been effectively used to prevent eavesdroppers from listening in to private conversations. In this study, we explore the problem of adversaries spying on smart home users to infer sensitive information with the aid of machine learning techniques. We then analyze the role of randomness in the effectiveness of sound masking for mitigating sensitive information leakage. We propose a Generative Adversarial Network (GAN) based approach for privacy preservation in smart homes which generates random noise to distort the unwanted machine learning-based inference. Our experimental results demonstrate that GANs can be used to generate more effective sound masking noise signals which exhibit more randomness and effectively mitigate deep learning-based inference attacks while preserving the semantics of the audio samples.
Building on the success of deep learning, Generative Adversarial Networks (GANs) provide a modern approach to learn a probability distribution from observed samples. GANs are often formulated as a zero-sum game between two sets of functions; the generator and the discriminator. Although GANs have shown great potentials in learning complex distributions such as images, they often suffer from the mode collapse issue where the generator fails to capture all existing modes of the input distribution. As a consequence, the diversity of generated samples is lower than that of the observed ones. To ta…
Innopolis University（因诺波利斯大学）；University of Oslo（奥斯陆大学）
Fairness has become an essential problem in many domains of Machine Learning (ML), such as classification, natural language processing, and Generative Adversarial Networks (GANs). In this research effort, we study the unfairness of GANs. We formally define a new fairness notion for generative models in terms of the distribution of generated samples sharing the same protected attributes (gender, race, etc.). The defined fairness notion (representational fairness) requires the distribution of the sensitive attributes at the test time to be uniform, and, in particular for GAN model, we show that …
University of California at Berkeley（加州大学伯克利分校）；Massachusetts Institute of Technology（麻省理工学院）；California Institute of Technology（加州理工学院）；Amazon Web Services（亚马逊网络服务）
Measuring biases of vision systems with respect to protected attributes like gender and age is critical as these systems gain widespread use in society. However, significant correlations between attributes in benchmark datasets make it difficult to separate algorithmic bias from dataset bias. To mitigate such attribute confounding during bias analysis, we propose a matching approach that selects a subset of images from the full dataset with balanced attribute distributions across protected attributes. Our matching approach first projects real images onto a generative adversarial network (GAN)'…
Pairing Regularization for Mitigating Many-to-One Collapse in GANs
Institute of Communications Engineering, National Yang Ming Chiao Tung University；Department of Electrical \& Computer Engineering, Texas A\&M University
University of Central Florida（中佛罗里达大学）；University of Ottawa（渥太华大学）
Data is the fuel of data science and machine learning techniques for smart grid applications, similar to many other fields. However, the availability of data can be an issue due to privacy concerns, data size, data quality, and so on. To this end, in this paper, we propose a Variational AutoEncoder Generative Adversarial Network (VAE-GAN) as a smart grid data generative model which is capable of learning various types of data distributions and generating plausible samples from the same distribution without performing any prior analysis on the data before the training phase.We compared the Kull…
Fondazione Bruno Kessler Research Institute（布鲁诺·凯斯勒基金会研究院）；University of Trento（特伦托大学）；University of Manchester（曼彻斯特大学）
Several approaches have been developed to mitigate algorithmic bias stemming from health data poverty, where minority groups are underrepresented in training datasets. Augmenting the minority class using resampling (such as SMOTE) is a widely used approach due to the simplicity of the algorithms. However, these algorithms decrease data variability and may introduce correlations between samples, giving rise to the use of generative approaches based on GAN. Generation of high-dimensional, time-series, authentic data that provides a wide distribution coverage of the real data, remains a challengi…
The use of synthetic data generated by Generative Adversarial Networks (GANs) has become quite a popular method to do data augmentation for many applications. While practitioners celebrate this as an economical way to get more synthetic data that can be used to train downstream classifiers, it is not clear that they recognize the inherent pitfalls of this technique. In this paper, we aim to exhort practitioners against deriving any false sense of security against data biases based on data augmentation. To drive this point home, we show that starting with a dataset consisting of head-shots of e…
Virginia Tech（弗吉尼亚理工大学）；Intelligent Automation, Inc.（智能自动化公司）
An adversarial machine learning approach is introduced to launch jamming attacks on wireless communications and a defense strategy is presented. A cognitive transmitter uses a pre-trained classifier to predict the current channel status based on recent sensing results and decides whether to transmit or not, whereas a jammer collects channel status and ACKs to build a deep learning classifier that reliably predicts the next successful transmissions and effectively jams them. This jamming approach is shown to reduce the transmitter's performance much more severely compared with random or sensing…
Norwegian University of Science and Technology（挪威科技大学）
While the whole world is still struggling with the COVID-19 pandemic, online learning and home office become more common. Many schools transfer their courses teaching to the online classroom. Therefore, it is significant to mine the students' feedback and opinions from their reviews towards studies so that both schools and teachers can know where they need to improve. This paper trains machine learning and deep learning models using both balanced and imbalanced datasets for sentiment classification. Two SOTA category-aware text generation GAN models: CatGAN and SentiGAN, are utilized to synthe…
Non-intrusive load monitoring (NILM) allows users and energy providers to gain insight into home appliance electricity consumption using only the building's smart meter. Most current techniques for NILM are trained using significant amounts of labeled appliances power data. The collection of such data is challenging, making data a major bottleneck in creating well generalizing NILM solutions. To help mitigate the data limitations, we present the first truly synthetic appliance power signature generator. Our solution, PowerGAN, is based on conditional, progressively growing, 1-D Wasserstein gen…
Distance Guided Generative Adversarial Network for Explainable Binary Classifications
Macao Polytechnic University（澳门理工大学）；Shanghai Jiao Tong University（上海交通大学）；East China Normal University（华东师范大学）；Zhejiang Sci-Tech University（浙江理工大学）；Hangzhou Normal University（杭州师范大学）
Application of intelligent systems especially in smart homes and health-related topics has been drawing more attention in the last decades. Training Human Activity Recognition (HAR) models -- as a major module -- requires a fair amount of labeled data. Despite training with large datasets, most of the existing models will face a dramatic performance drop when they are tested against unseen data from new users. Moreover, recording enough data for each new user is unviable due to the limitations and challenges of working with human users. Transfer learning techniques aim to transfer the knowledg…
University of Colorado Boulder（科罗拉多大学博尔德分校）；Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；University of California Berkeley（加州大学伯克利分校）；James Madison University（詹姆斯·麦迪逊大学）
A dry decade in the Navajo Nation has killed vegetation, dessicated soils, and released once-stable sand into the wind. This sand now covers one-third of the Nation's land, threatening roads, gardens and hundreds of homes. Many arid regions have similar problems: global warming has increased dune movement across farmland in Namibia and Angola, and the southwestern US. Current dune models, unfortunately, do not scale well enough to provide useful forecasts for the $\sim$5\% of land surfaces covered by mobile sand. We test the ability of two deep learning algorithms, a GAN and a CNN, to model th…
HURRI-GAN: A Novel Approach for Hurricane Bias-Correction Beyond Gauge Stations using Generative Adversarial Networks
Center of Computation and Technology, Louisiana State University（路易斯安那州立大学计算与技术中心）；Saint-Joseph University of Beirut（贝鲁特圣约瑟夫大学）；Department of Physics, Aristotle University of Thessaloniki（塞萨洛尼基阿列克斯大学物理系）；Center for Interdisciplinary Research and Innovation, Aristotle University of Thessaloniki（塞萨洛尼基阿列克斯大学跨学科研究中心）；Department of Computer Science, Louisiana State University（路易斯安那州立大学计算机科学系）；Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin（德克萨斯大学奥斯汀分校奥登计算工程与科学研究院）
Synthetic data generation has emerged as a promising approach to address the challenges of using sensitive financial data in machine learning applications. By leveraging generative models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), it is possible to create artificial datasets that preserve the statistical properties of real financial records while mitigating privacy risks and regulatory constraints. Despite the rapid growth of this field, a comprehensive synthesis of the current research landscape has been lacking. This systematic review consolidates an…
Google DeepMind（谷歌DeepMind）；ETHZ（苏黎世联邦理工学院）；Princeton（普林斯顿大学）；UC Berkeley（加州大学伯克利分校）
Image diffusion models such as DALL-E 2, Imagen, and Stable Diffusion have attracted significant attention due to their ability to generate high-quality synthetic images. In this work, we show that diffusion models memorize individual images from their training data and emit them at generation time. With a generate-and-filter pipeline, we extract over a thousand training examples from state-of-the-art models, ranging from photographs of individual people to trademarked company logos. We also train hundreds of diffusion models in various settings to analyze how different modeling and data decis…
Millions of vulnerable consumer IoT devices in home networks are the enabler for cyber crimes putting user privacy and Internet security at risk. Internet service providers (ISPs) are best poised to play key roles in mitigating risks by automatically inferring active IoT devices per household and notifying users of vulnerable ones. Developing a scalable inference method that can perform robustly across thousands of home networks is a non-trivial task. This paper focuses on the challenges of developing and applying data-driven inference models when labeled data of device behaviors is limited an…
University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；University of Washington（华盛顿大学）
Voice assistants are deployed widely and provide useful functionality. However, recent work has shown that commercial systems like Amazon Alexa and Google Home are vulnerable to voice-based confusion attacks that exploit design issues. We propose a systems-oriented defense against this class of attacks and demonstrate its functionality for Amazon Alexa. We ensure that only the skills a user intends execute in response to voice commands. Our key insight is that we can interpret a user's intentions by analyzing their activity on counterpart systems of the web and smartphones. For example, the Ly…
FFHFlow: Diverse and Uncertainty-Aware Dexterous Grasp Generation via Flow Variational Inference
School of Information Computation and Technology, Technical University of Munich (TUM)（技术大学慕尼黑信息计算技术学院）；Institute of Robotics and Mechatronics, German Aerospace Center (DLR)（德国航空航天中心机器人与机电研究所）；Department of Informatics, Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院信息学院）
Bed-Attached Vibration Sensor System: A Machine Learning Approach for Fall Detection in Nursing Homes
Institute IDE+A, TH Köln（科隆应用技术大学IDE+A研究所）；Institut für Allgemeinen Maschinenbau, TH Köln（科隆应用技术大学普通机械工程研究所）；Federal University of Applied Administrative Sciences（联邦应用行政科学大学）；tecfor care GmbH（tecfor care有限公司）
