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Voice assistants are becoming increasingly pervasive due to the convenience and automation they provide through the voice interface. However, such convenience often comes with unforeseen security and privacy risks. For example, encrypted traffic from voice assistants can leak sensitive information about their users' habits and lifestyles. In this paper, we present a taxonomy of fingerprinting voice commands on the most popular voice assistant platforms (Google, Alexa, and Siri). We also provide a deeper understanding of the feasibility of fingerprinting third-party applications and streaming services over the voice interface. Our analysis not only improves the state-of-the-art technique but also studies a more realistic setup for fingerprinting voice activities over encrypted traffic.Our proposed technique considers a passive network eavesdropper observing encrypted traffic from various devices within a home and, therefore, first detects the invocation/activation of voice assistants followed by what specific voice command is issued. Using an end-to-end system design, we show that it is possible to detect when a voice assistant is activated with 99% accuracy and then utilize the subsequent traffic pattern to infer more fine-grained user activities with around 77-80% accuracy. 

The Amazon Alexa voice assistant provides convenience through automation and control of smart home appliances using voice commands. Amazon allows third-party applications known as skills to run on top of Alexa to further extend Alexa's capability. However, as multiple skills can share the same invocation phrase and request access to sensitive user data, growing security and privacy concerns surround third-party skills. In this paper, we study the availability and effectiveness of existing security indicators or a lack thereof to help users properly comprehend the risk of interacting with different types of skills. We conduct an interactive user study (inviting active users of Amazon Alexa) where participants listen to and interact with real-world skills using the official Alexa app. We find that most participants fail to identify the skill developer correctly (i.e., they assume Amazon also develops the third-party skills) and cannot correctly determine which skills will be automatically activated through the voice interface. We also propose and evaluate a few voice-based skill type indicators, showcasing how users would benefit from such voice-based indicators. 

Abstract In recent years, we have seen rapid growth in the use and adoption of Internet of Things (IoT) devices. However, some loT devices are sensitive in nature, and simply knowing what devices a user owns can have security and privacy implications. Researchers have, therefore, looked at fingerprinting loT devices and their activities from encrypted network traffic. In this paper, we analyze the feasibility of fingerprinting IoT devices and evaluate the robustness of such fingerprinting approach across multiple independent datasets — collected under different settings. We show that not only is it possible to effectively fingerprint 188 loT devices (with over 97% accuracy), but also to do so even with multiple instances of the same make-and-model device. We also analyze the extent to which temporal, spatial and data-collection-methodology differences impact fingerprinting accuracy. Our analysis sheds light on features that are more robust against varying conditions. Lastly, we comprehensively analyze the performance of our approach under an open-world setting and propose ways in which an adversary can enhance their odds of inferring additional information about unseen devices (e.g., similar devices manufactured by the same company). 

Targeted advertisement is prevalent on the Web. Many privacy-enhancing tools have been developed to thwart targeted advertisement. Adblock Plus is one such popular tool, used by millions of users on a daily basis, to block unwanted ads and trackers. Adblock Plus uses EasyList and EasyPrivacy, the most prominent and widely used open-source filters, to block unwanted web contents. However, Adblock Plus, by default, also enables an exception list to unblock web requests that comply with specific guidelines defined by the Acceptable Ads Committee. Any publisher can enroll into the Acceptable Ads initiative to request the unblocking of web contents. Adblock Plus in return charges a licensing fee from large entities, who gain a significant amount of ad impressions per month due to participation in the Acceptable Ads initiative. However, the privacy implications of the default inclusion of the exception list has not been well studied, especially as it can unblock not only ads, but also trackers (e.g., unblocking contents otherwise blocked by EasyPrivacy). In this paper, we take a data-driven approach, where we collect historical updates made to Adblock Plus's exception list and real-world web traffic by visiting the top 10k websites listed by Tranco. Using such data we analyze not only how the exception list has evolved over the years in terms of both contents unblocked and partners/entities enrolled into the Acceptable Ads initiative, but also the privacy implications of enabling the exception list by default. We found that Google not only unblocks the most number of unique domains, but is also unblocked by the most number of unique partners. From our traffic analysis, we see that of the 42,210 Google bound web requests, originally blocked by EasyPrivacy, around 80% of such requests are unblocked by the exception list. More worryingly, many of the requests enable 1-by-1 tracking pixel images. We, therefore, question exception rules that negate EasyPrivacy filtering rules by default and advocate for a better vetting process. 

The proliferation of the Internet of Things (IoT) has started transforming our lifestyle through automation of home appliances. However, there are users who are hesitant to adopt IoT devices due to various privacy and security concerns. In this paper, we elicit peoples’ attitude and concerns towards adopting IoT devices. We conduct an online survey and collect responses from 232 participants from three different geographic regions (United States, Europe, and India); the participants consist of both adopters and non-adopters of IoT devices. Through data analysis, we determine that there are both similarities and differences in perceptions and concerns between adopters and non-adopters. For example, even though IoT and non-IoT users share similar security and privacy concerns, IoT users are more comfortable using IoT devices in private settings compared to non-IoT users. Furthermore, when comparing users’ attitude and concerns across different geographic regions, we found similarities between participants from the US and Europe, yet participants from India showcased contrasting behavior. For instance, we found that participants from India were more trusting in their government to properly protect consumer data and were more comfortable using IoT devices in a variety of public settings, compared to participants from the US and Europe. Based on our findings, we provide recommendations to reduce users’ concerns in adopting IoT devices, and thereby enhance user trust towards adopting IoT devices. 

Amazon's voice-based assistant, Alexa, enables users to directly interact with various web services through natural language dialogues. It provides developers with the option to create third-party applications (known as Skills) to run on top of Alexa. While such applications ease users' interaction with smart devices and bolster a number of additional services, they also raise security and privacy concerns due to the personal setting they operate in. This paper aims to perform a systematic analysis of the Alexa skill ecosystem. We perform the first large-scale analysis of Alexa skills, obtained from seven different skill stores totaling to 90,194 unique skills. Our analysis reveals several limitations that exist in the current skill vetting process. We show that not only can a malicious user publish a skill under any arbitrary developer/company name, but she can also make backend code changes after approval to coax users into revealing unwanted information. We, next, formalize the different skill-squatting techniques and evaluate the efficacy of such techniques. We find that while certain approaches are more favorable than others, there is no substantial abuse of skill squatting in the real world. Lastly, we study the prevalence of privacy policies across different categories of skill, and more importantly the policy content of skills that use the Alexa permission model to access sensitive user data. We find that around 23.3% of such skills do not fully disclose the data types associated with the permissions requested. We conclude by providing some suggestions for strengthening the overall ecosystem, and thereby enhance transparency for end-users.