The pervasive use of smart speakers has raised numerous privacy concerns. While work to date provides an understanding of user perceptions of these threats, limited research focuses on how we can mitigate these concerns, either through redesigning the smart speaker or through dedicated privacy-preserving interventions. In this paper, we present the design and prototyping of two privacy-preserving interventions: `Obfuscator' targeted at disabling recording at the microphones, and `PowerCut' targeted at disabling power to the smart speaker. We present our findings from a technology probe study involving 24 households that interacted with our prototypes; the primary objective was to gain a better understanding of the design space for technological interventions that might address these concerns. Our data and findings reveal complex trade-offs among utility, privacy, and usability and stresses the importance of multi-functionality, aesthetics, ease-of-use, and form factor. We discuss the implications of our findings for the development of subsequent interventions and the future design of smart speakers.
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In 21st century America, to many observers, the idea that 10's of millions of Americans could lose power and heat for multiple days in the middle of a record cold snap, was unthinkable. It came as an even greater surprise that it would be Texas - arguably one of the world's energy capitals - that failed to provide sufficient power to its residents. This paper explores the events that led to the outage, the experiences of those who lived through it, and the situation in Texas one to two months after the event. We have taken an ethnographic approach to capture both the empirical aspects of the situation, and the more interpretive descriptions of the accounts and thoughts of the participants. We believe this ethnography of events in Texas can serve as foundational evidence and therefore can be generalized to a wide variety of situations and methodologies.
An important issue for many economic experiments is how the experimenter can ensure sufficient power for rejecting one or more hypotheses. Here, we apply methods developed mainly within the area of clinical trials for testing multiple hypotheses simultaneously in adaptive, two-stage designs. Our main goal is to illustrate how this approach can be used to improve the power of economic experiments. Having briefly introduced the relevant theory, we perform a simulation study supported by the open source R package asd in order to evaluate the power of some different designs. The simulations show that the power to reject at least one hypothesis can be improved while still ensuring strong control of the overall Type I error probability, and without increasing the total sample size and thus the costs of the study. The derived designs are further illustrated by applying them to two different real-world data sets from experimental economics.
Third-party libraries (TPLs) have become a significant part of the Android ecosystem. Developers can employ various TPLs to facilitate their app development. Unfortunately, the popularity of TPLs also brings new security issues. For example, TPLs may carry malicious or vulnerable code, which can infect popular apps to pose threats to mobile users. Furthermore, TPL detection is essential for downstream tasks, such as vulnerabilities and malware detection. Thus, various tools have been developed to identify TPLs. However, no existing work has studied these TPL detection tools in detail, and different tools focus on different applications and techniques with performance differences. A comprehensive understanding of these tools will help us make better use of them. To this end, we conduct a comprehensive empirical study to fill the gap by evaluating and comparing all publicly available TPL detection tools based on six criteria: accuracy of TPL construction, effectiveness, efficiency, accuracy of version identification, resiliency to code obfuscation, and ease of use. Besides, we enhance these open-source tools by fixing their limitations, to improve their detection ability. Finally, we build an extensible framework that integrates all existing available TPL detection tools, providing an online service for the research community. We release the evaluation dataset and enhanced tools. According to our study, we also present the essential findings and discuss promising implications to the community. We believe our work provides a clear picture of existing TPL detection techniques and also gives a roadmap for future research.
Due to the ever-increasing threat of cyber-attacks to critical cyber infrastructure, organizations are focusing on building their cybersecurity knowledge base. A salient list of cybersecurity knowledge is the Common Vulnerabilities and Exposures (CVE) list, which details vulnerabilities found in a wide range of software and hardware. However, these vulnerabilities often do not have a mitigation strategy to prevent an attacker from exploiting them. A well-known cybersecurity risk management framework, MITRE ATT&CK, offers mitigation techniques for many malicious tactics. Despite the tremendous benefits that both CVEs and the ATT&CK framework can provide for key cybersecurity stakeholders (e.g., analysts, educators, and managers), the two entities are currently separate. We propose a model, named the CVE Transformer (CVET), to label CVEs with one of ten MITRE ATT&CK tactics. The CVET model contains a fine-tuning and self-knowledge distillation design applied to the state-of-the-art pre-trained language model RoBERTa. Empirical results on a gold-standard dataset suggest that our proposed novelties can increase model performance in F1-score. The results of this research can allow cybersecurity stakeholders to add preliminary MITRE ATT&CK information to their collected CVEs.
Federated learning (FL) is an emerging, privacy-preserving machine learning paradigm, drawing tremendous attention in both academia and industry. A unique characteristic of FL is heterogeneity, which resides in the various hardware specifications and dynamic states across the participating devices. Theoretically, heterogeneity can exert a huge influence on the FL training process, e.g., causing a device unavailable for training or unable to upload its model updates. Unfortunately, these impacts have never been systematically studied and quantified in existing FL literature. In this paper, we carry out the first empirical study to characterize the impacts of heterogeneity in FL. We collect large-scale data from 136k smartphones that can faithfully reflect heterogeneity in real-world settings. We also build a heterogeneity-aware FL platform that complies with the standard FL protocol but with heterogeneity in consideration. Based on the data and the platform, we conduct extensive experiments to compare the performance of state-of-the-art FL algorithms under heterogeneity-aware and heterogeneity-unaware settings. Results show that heterogeneity causes non-trivial performance degradation in FL, including up to 9.2% accuracy drop, 2.32x lengthened training time, and undermined fairness. Furthermore, we analyze potential impact factors and find that device failure and participant bias are two potential factors for performance degradation. Our study provides insightful implications for FL practitioners. On the one hand, our findings suggest that FL algorithm designers consider necessary heterogeneity during the evaluation. On the other hand, our findings urge system providers to design specific mechanisms to mitigate the impacts of heterogeneity.
GAN inversion aims to invert a given image back into the latent space of a pretrained GAN model, for the image to be faithfully reconstructed from the inverted code by the generator. As an emerging technique to bridge the real and fake image domains, GAN inversion plays an essential role in enabling the pretrained GAN models such as StyleGAN and BigGAN to be used for real image editing applications. Meanwhile, GAN inversion also provides insights on the interpretation of GAN's latent space and how the realistic images can be generated. In this paper, we provide an overview of GAN inversion with a focus on its recent algorithms and applications. We cover important techniques of GAN inversion and their applications to image restoration and image manipulation. We further elaborate on some trends and challenges for future directions.
As data are increasingly being stored in different silos and societies becoming more aware of data privacy issues, the traditional centralized training of artificial intelligence (AI) models is facing efficiency and privacy challenges. Recently, federated learning (FL) has emerged as an alternative solution and continue to thrive in this new reality. Existing FL protocol design has been shown to be vulnerable to adversaries within or outside of the system, compromising data privacy and system robustness. Besides training powerful global models, it is of paramount importance to design FL systems that have privacy guarantees and are resistant to different types of adversaries. In this paper, we conduct the first comprehensive survey on this topic. Through a concise introduction to the concept of FL, and a unique taxonomy covering: 1) threat models; 2) poisoning attacks and defenses against robustness; 3) inference attacks and defenses against privacy, we provide an accessible review of this important topic. We highlight the intuitions, key techniques as well as fundamental assumptions adopted by various attacks and defenses. Finally, we discuss promising future research directions towards robust and privacy-preserving federated learning.
The field of Text-to-Speech has experienced huge improvements last years benefiting from deep learning techniques. Producing realistic speech becomes possible now. As a consequence, the research on the control of the expressiveness, allowing to generate speech in different styles or manners, has attracted increasing attention lately. Systems able to control style have been developed and show impressive results. However the control parameters often consist of latent variables and remain complex to interpret. In this paper, we analyze and compare different latent spaces and obtain an interpretation of their influence on expressive speech. This will enable the possibility to build controllable speech synthesis systems with an understandable behaviour.
We detail a new framework for privacy preserving deep learning and discuss its assets. The framework puts a premium on ownership and secure processing of data and introduces a valuable representation based on chains of commands and tensors. This abstraction allows one to implement complex privacy preserving constructs such as Federated Learning, Secure Multiparty Computation, and Differential Privacy while still exposing a familiar deep learning API to the end-user. We report early results on the Boston Housing and Pima Indian Diabetes datasets. While the privacy features apart from Differential Privacy do not impact the prediction accuracy, the current implementation of the framework introduces a significant overhead in performance, which will be addressed at a later stage of the development. We believe this work is an important milestone introducing the first reliable, general framework for privacy preserving deep learning.
Privacy is a major good for users of personalized services such as recommender systems. When applied to the field of health informatics, privacy concerns of users may be amplified, but the possible utility of such services is also high. Despite availability of technologies such as k-anonymity, differential privacy, privacy-aware recommendation, and personalized privacy trade-offs, little research has been conducted on the users' willingness to share health data for usage in such systems. In two conjoint-decision studies (sample size n=521), we investigate importance and utility of privacy-preserving techniques related to sharing of personal health data for k-anonymity and differential privacy. Users were asked to pick a preferred sharing scenario depending on the recipient of the data, the benefit of sharing data, the type of data, and the parameterized privacy. Users disagreed with sharing data for commercial purposes regarding mental illnesses and with high de-anonymization risks but showed little concern when data is used for scientific purposes and is related to physical illnesses. Suggestions for health recommender system development are derived from the findings.
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