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Deep learning has shown incredible potential across a wide array of tasks, and accompanied by this growth has been an insatiable appetite for data. However, a large amount of data needed for enabling deep learning is stored on personal devices, and recent concerns on privacy have further highlighted challenges for accessing such data. As a result, federated learning (FL) has emerged as an important privacy-preserving technology that enables collaborative training of machine learning models without the need to send the raw, potentially sensitive, data to a central server. However, the fundamental premise that sending model updates to a server is privacy-preserving only holds if the updates cannot be “reverse engineered” to infer information about the private training data. It has been shown under a wide variety of settings that this privacy premise doesnothold. In this article we provide a comprehensive literature review of the different privacy attacks and defense methods in FL. We identify the current limitations of these attacks and highlight the settings in which the privacy of an FL client can be broken. We further dissect some of the successful industry applications of FL and draw lessons for future successful adoption. We survey the emerging landscape of privacy regulation for FL and conclude with future directions for taking FL toward the cherished goal of generating accurate models while preserving the privacy of the data from its participants. 

Abstract In this work, we explore multiplex graph (networks with different types of edges) generation with deep generative models. We discuss some of the challenges associated with multiplex graph generation that make it a more difficult problem than traditional graph generation. We propose TenGAN, the first neural network for multiplex graph generation, which greatly reduces the number of parameters required for multiplex graph generation. We also propose 3 different criteria for evaluating the quality of generated graphs: a graph-attribute-based, a classifier-based, and a tensor-based method. We evaluate its performance on 4 datasets and show that it generally performs better than other existing statistical multiplex graph generative models. We also adapt HGEN, an existing deep generative model for heterogeneous information networks, to work for multiplex graphs and show that our method generally performs better. 

Online advertisers have been quite successful in circumventing traditional adblockers that rely on manually curated rules to detect ads. As a result, adblockers have started to use machine learning (ML) classifiers for more robust detection and blocking of ads. Among these, AdGraph which leverages rich contextual information to classify ads, is arguably, the state of the art ML-based adblocker. In this paper, we present a4, a tool that intelligently crafts adversarial ads to evade AdGraph. Unlike traditional adversarial examples in the computer vision domain that can perturb any pixels (i.e., unconstrained), adversarial ads generated by a4 are actionable in the sense that they preserve the application semantics of the web page. Through a series of experiments we show that a4 can bypass AdGraph about 81% of the time, which surpasses the state-of-the-art attack by a significant margin of 145.5%, with an overhead of <20% and perturbations that are visually imperceptible in the rendered webpage. We envision that a4’s framework can be used to potentially launch adversarial attacks against other ML-based web applications.