arXiv subjects

Nathan Malkin

Publications and source records attributed to Nathan Malkin.

4 recordsLinked to original sources

arXiv2026

A Penny for Your Prompts: Experiments Detecting and Mitigating LLM Usage by Survey Respondents

Large language models are increasingly used by participants on crowdsourcing platforms when responding to surveys, potentially undermining the validity of collected data. Our study aims to quantify the prevalence of this behavior and investigate methods to detect and prevent it. In a series of surveys (N = 250), we examined conditions such as platform choice, survey length, requests not to use AI, and disabling copy-paste functionality. We were able to identify distinct characteristics of LLM-assisted responses and found that their frequency varied widely, from under 10% on Prolific to over 80% on Mechanical Turk. Mitigation measures reduced LLM usage but did not necessarily improve data quality. No participants employed browser-use agents at the time of our survey, but we report on our own detection experiments. We recommend that researchers actively screen survey responses for LLM usage by recording and analyzing keystroke data and crafting instructions and questions aimed at AI.

cs.HC↗

arXiv2025

Characterizing Security and Privacy Teaching Standards for Schools in the United States

Increasingly, students begin learning aspects of security and privacy during their primary and secondary education (grades K-12 in the United States). Individual U.S. states and some national organizations publish teaching standards -- guidance that outlines expectations for what students should learn -- which often form the basis for course curricula. However, research has not yet examined what is covered by these standards and whether the topics align with what the broader security and privacy community thinks students should know. To shed light on these questions, we started by collecting computer science teaching standards from all U.S. states and eight national organizations. After manually examining a total of 11,954 standards, we labeled 3,778 of them as being related to security and privacy, further classifying these into 103 topics. Topics ranged from technical subjects like encryption, network security, and embedded systems to social subjects such as laws, ethics, and appropriate online behavior. Subsequently, we interviewed 11 security and privacy professionals to examine how the teaching standards align with their expectations. We found that, while the specific topics they mentioned mostly overlapped with those of existing standards, professionals placed a greater emphasis on threat modeling and security mindset.

cs.CR↗

arXiv2021

SoK: A Framework for Unifying At-Risk User Research

At-risk users are people who experience elevated digital security, privacy, and safety threats because of what they do, who they are, where they are, or who they are with. In this systematization work, we present a framework for reasoning about at-risk users based on a wide-ranging meta-analysis of 85 papers. Across the varied populations that we examined (e.g., children, activists, women in developing regions), we identified 10 unifying contextual risk factors--such as oppression or stigmatization and access to a sensitive resource--which augment or amplify digital-safety threats and their resulting harms. We also identified technical and non-technical practices that at-risk users adopt to attempt to protect themselves from digital-safety threats. We use this framework to discuss barriers that limit at-risk users' ability or willingness to take protective actions. We believe that the security, privacy, and human-computer interaction research and practitioner communities can use our framework to identify and shape research investments to benefit at-risk users, and to guide technology design to better support at-risk users.

cs.CY↗

arXiv2018

Quantifying Users' Beliefs about Software Updates

Software updates are critical to the performance, compatibility, and security of software systems. However, users do not always install updates, leaving their machines vulnerable to attackers' exploits. While recent studies have highlighted numerous reasons why users ignore updates, little is known about how prevalent each of these beliefs is. Gaining a better understanding of the prevalence of each belief may help software designers better target their efforts in understanding what specific user concerns to address when developing and deploying software updates. In our study, we performed a survey to quantify the prevalence of users' reasons for not updating uncovered by previous studies. We used this data to derive three factors underlying these beliefs: update costs, update necessity, and update risks. Based on our results, we provide recommendations for how software developers can better improve users' software updating experiences, thereby increasing compliance and, with it, security.

cs.CR↗