arXiv subjects

Brian Tang

Publications and source records attributed to Brian Tang.

9 recordsLinked to original sources

arXiv2025

Navigating Cookie Consent Violations Across the Globe

Online services provide users with cookie banners to accept/reject the cookies placed on their web browsers. Despite the increased adoption of cookie banners, little has been done to ensure that cookie consent is compliant with privacy laws around the globe. Prior studies have found that cookies are often placed on browsers even after their explicit rejection by users. These inconsistencies in cookie banner behavior circumvent users' consent preferences and are known as cookie consent violations. To address this important problem, we propose an end-to-end system, called ConsentChk, that detects and analyzes cookie banner behavior. ConsentChk uses a formal model to systematically detect and categorize cookie consent violations. We investigate eight English-speaking regions across the world, and analyze cookie banner behavior across 1,793 globally-popular websites. Cookie behavior, cookie consent violation rates, and cookie banner implementations are found to be highly dependent on region. Our evaluation reveals that consent management platforms (CMPs) and website developers likely tailor cookie banner configurations based on their (often incorrect) interpretations of regional privacy laws. We discuss various root causes behind these cookie consent violations. The resulting implementations produce misleading cookie banners, indicating the prevalence of inconsistently implemented and enforced cookie consent between various regions.

cs.CR↗

arXiv2024

Steward: Natural Language Web Automation

Recently, large language models (LLMs) have demonstrated exceptional capabilities in serving as the foundation for AI assistants. One emerging application of LLMs, navigating through websites and interacting with UI elements across various web pages, remains somewhat underexplored. We introduce Steward, a novel LLM-powered web automation tool designed to serve as a cost-effective, scalable, end-to-end solution for automating web interactions. Traditional browser automation frameworks like Selenium, Puppeteer, and Playwright are not scalable for extensive web interaction tasks, such as studying recommendation algorithms on platforms like YouTube and Twitter. These frameworks require manual coding of interactions, limiting their utility in large-scale or dynamic contexts. Steward addresses these limitations by integrating LLM capabilities with browser automation, allowing for natural language-driven interaction with websites. Steward operates by receiving natural language instructions and reactively planning and executing a sequence of actions on websites, looping until completion, making it a practical tool for developers and researchers to use. It achieves high efficiency, completing actions in 8.52 to 10.14 seconds at a cost of $0.028 per action or an average of $0.18 per task, which is further reduced to 4.8 seconds and $0.022 through a caching mechanism. It runs tasks on real websites with a 40% completion success rate. We discuss various design and implementation challenges, including state representation, action sequence selection, system responsiveness, detecting task completion, and caching implementation.

cs.AI↗

arXiv2024

Analyzing Privacy Implications of Data Collection in Android Automotive OS

Modern vehicles have become sophisticated computation and sensor systems, as evidenced by advanced driver assistance systems, in-car infotainment, and autonomous driving capabilities. They collect and process vast amounts of data through various embedded subsystems. One significant player in this landscape is Android Automotive OS (AAOS), which has been integrated into over 100M vehicles and has become a dominant force in the in-vehicle infotainment market. With this extensive data collection, privacy has become increasingly crucial. The volume of data gathered by these systems raises questions about how this information is stored, used, and protected, making privacy a critical issue for manufacturers and consumers. However, very little has been done on vehicle data privacy. This paper focuses on the privacy implications of AAOS, examining the exact nature and scope of data collection and the corresponding privacy policies from the original equipment manufacturers (OEMs). We develop a novel automotive privacy analysis tool called PriDrive which employs three methodological approaches: network traffic inspection, and both static and dynamic analyses of Android images using rooted emulators from various OEMs. These methodologies are followed by an assessment of whether the collected data types were properly disclosed in OEMs and 3rd party apps' privacy policies (to identify any discrepancies or violations). Our evaluation on three different OEM platforms reveals that vehicle speed is collected at a sampling rate of roughly 25 Hz. Other properties such as model info, climate & AC, and seat data are collected in a batch 30 seconds into vehicle startup. In addition, several vehicle property types were collected without disclosure in their respective privacy policies. For example, OEM A's policies only covers 110 vehicle properties or 13.02% of the properties found in our static analysis.

cs.CR↗

arXiv2023

Eye-Shield: Real-Time Protection of Mobile Device Screen Information from Shoulder Surfing

People use mobile devices ubiquitously for computing, communication, storage, web browsing, and more. As a result, the information accessed and stored within mobile devices, such as financial and health information, text messages, and emails, can often be sensitive. Despite this, people frequently use their mobile devices in public areas, becoming susceptible to a simple yet effective attack, shoulder surfing. Shoulder surfing occurs when a person near a mobile user peeks at the user's mobile device, potentially acquiring passcodes, PINs, browsing behavior, or other personal information. We propose Eye-Shield, a solution to prevent shoulder surfers from accessing or stealing sensitive on-screen information. Eye-Shield is designed to protect all types of on-screen information in real time, without any serious impediment to users' interactions with their mobile devices. Eye-Shield generates images that appear readable at close distances, but appear blurry or pixelated at farther distances and wider angles. It is capable of protecting on-screen information from shoulder surfers, operating in real time, and being minimally intrusive to the intended users. Eye-Shield protects images and text from shoulder surfers by reducing recognition rates to 24.24% and 15.91%. Our implementations of Eye-Shield, with frame rates of 24 FPS for Android and 43 FPS for iOS, effectively work on screen resolutions as high as 1440x3088. Eye-Shield also incurs acceptable memory usage, CPU utilization, and energy overhead. Finally, our MTurk and in-person user studies indicate that Eye-Shield protects on-screen information without a large usability cost for privacy-conscious users.

cs.CR↗

arXiv2022

Reproducible Subjective Evaluation

Human perceptual studies are the gold standard for the evaluation of many research tasks in machine learning, linguistics, and psychology. However, these studies require significant time and cost to perform. As a result, many researchers use objective measures that can correlate poorly with human evaluation. When subjective evaluations are performed, they are often not reported with sufficient detail to ensure reproducibility. We propose Reproducible Subjective Evaluation (ReSEval), an open-source framework for quickly deploying crowdsourced subjective evaluations directly from Python. ReSEval lets researchers launch A/B, ABX, Mean Opinion Score (MOS) and MUltiple Stimuli with Hidden Reference and Anchor (MUSHRA) tests on audio, image, text, or video data from a command-line interface or using one line of Python, making it as easy to run as objective evaluation. With ReSEval, researchers can reproduce each other's subjective evaluations by sharing a configuration file and the audio, image, text, or video files.

cs.HC↗

arXiv2022

CONFIDANT: A Privacy Controller for Social Robots

As social robots become increasingly prevalent in day-to-day environments, they will participate in conversations and appropriately manage the information shared with them. However, little is known about how robots might appropriately discern the sensitivity of information, which has major implications for human-robot trust. As a first step to address a part of this issue, we designed a privacy controller, CONFIDANT, for conversational social robots, capable of using contextual metadata (e.g., sentiment, relationships, topic) from conversations to model privacy boundaries. Afterwards, we conducted two crowdsourced user studies. The first study (n=174) focused on whether a variety of human-human interaction scenarios were perceived as either private/sensitive or non-private/non-sensitive. The findings from our first study were used to generate association rules. Our second study (n=95) evaluated the effectiveness and accuracy of the privacy controller in human-robot interaction scenarios by comparing a robot that used our privacy controller against a baseline robot with no privacy controls. Our results demonstrate that the robot with the privacy controller outperforms the robot without the privacy controller in privacy-awareness, trustworthiness, and social-awareness. We conclude that the integration of privacy controllers in authentic human-robot conversations can allow for more trustworthy robots. This initial privacy controller will serve as a foundation for more complex solutions.

cs.RO↗

arXiv2021

Fairness Properties of Face Recognition and Obfuscation Systems

The proliferation of automated face recognition in the commercial and government sectors has caused significant privacy concerns for individuals. One approach to address these privacy concerns is to employ evasion attacks against the metric embedding networks powering face recognition systems: Face obfuscation systems generate imperceptibly perturbed images that cause face recognition systems to misidentify the user. Perturbed faces are generated on metric embedding networks, which are known to be unfair in the context of face recognition. A question of demographic fairness naturally follows: are there demographic disparities in face obfuscation system performance? We answer this question with an analytical and empirical exploration of recent face obfuscation systems. Metric embedding networks are found to be demographically aware: face embeddings are clustered by demographic. We show how this clustering behavior leads to reduced face obfuscation utility for faces in minority groups. An intuitive analytical model yields insight into these phenomena.

cs.CV↗

arXiv2020

Face-Off: Adversarial Face Obfuscation

Advances in deep learning have made face recognition technologies pervasive. While useful to social media platforms and users, this technology carries significant privacy threats. Coupled with the abundant information they have about users, service providers can associate users with social interactions, visited places, activities, and preferences--some of which the user may not want to share. Additionally, facial recognition models used by various agencies are trained by data scraped from social media platforms. Existing approaches to mitigate these privacy risks from unwanted face recognition result in an imbalanced privacy-utility trade-off to users. In this paper, we address this trade-off by proposing Face-Off, a privacy-preserving framework that introduces strategic perturbations to the user's face to prevent it from being correctly recognized. To realize Face-Off, we overcome a set of challenges related to the black-box nature of commercial face recognition services, and the scarcity of literature for adversarial attacks on metric networks. We implement and evaluate Face-Off to find that it deceives three commercial face recognition services from Microsoft, Amazon, and Face++. Our user study with 423 participants further shows that the perturbations come at an acceptable cost for the users.

cs.CR↗

arXiv2019

Rearchitecting Classification Frameworks For Increased Robustness

While generalizing well over natural inputs, neural networks are vulnerable to adversarial inputs. Existing defenses against adversarial inputs have largely been detached from the real world. These defenses also come at a cost to accuracy. Fortunately, there are invariances of an object that are its salient features; when we break them it will necessarily change the perception of the object. We find that applying invariants to the classification task makes robustness and accuracy feasible together. Two questions follow: how to extract and model these invariances? and how to design a classification paradigm that leverages these invariances to improve the robustness accuracy trade-off? The remainder of the paper discusses solutions to the aformenetioned questions.

cs.LG↗