Browse research papers, practitioner briefs, and other outputs.
Examines how streaming fragmentation relates to earlier leaks, larger torrent swarms, and increased user risk in piracy ecosystems.
Introduces CLPIPS, a customized perceptual similarity metric tuned against human judgments for AI-generated image comparison tasks.
Examines risks job seekers face when relying on LLM-generated employer insights, including potential distortion of employer image signals and information quality.
Studies how online word-of-mouth on Glassdoor shapes organizational image and applicant behavior in recruitment contexts.
Evaluates human ability to infer the prompts used to generate AI art. Findings show that prompt inference is difficult and that contextual factors significantly affect performance.
Proposes a taxonomy for evaluating dataset value in AI-native empirical research. Moves beyond volume-centric data paradigms to consider dimensions of quality, representativeness, and ethical sourcing.
We audit large companies' privacy and data sharing practices through email marketing. The study reveals systematic data sharing with third parties and evaluates implications for user privacy.
Presents EtherBee, a global-scale dataset coupling Ethereum node performance measurements with honeypot interactions and full network sessions. Enables research on blockchain infrastructure security and performance.
Presents initial evidence of widespread, systematic reconnaissance targeting Ethereum node infrastructure. Measurement data reveals that blockchain nodes face pervasive scanning and probing from adversarial actors.
Introduces MagnetDB, a 15-year longitudinal torrent-discovery dataset (4,936,257 magnet links) matched with IMDb metadata, enabling reproducible research on piracy dynamics, release timing, and P2P behavior.
Evaluates the efficacy of iterative human-driven prompt refinement in AI image regeneration tasks. Findings reveal both the potential and limits of human ability to reverse-engineer prompts from generated images.
Funded research to build real-time surveillance and changepoint-based anomaly detection for Ethereum peer-to-peer network security.
Funded design and evaluation of an overlay networking approach to improve Ethereum node anonymity and resilience against network-layer targeting.
Presents NinjaDoH, a moving-target DNS-over-HTTPS architecture that combines hyperscaler deployment with IPNS-based endpoint agility to improve censorship resistance and availability.
Funded work on RADS: Rapid Activation via Decentralized Snapshots, a decentralized storage pipeline for Ethereum snapshot delivery and recovery.
Captures migration governance decisions for Helium's transition from a sovereign Layer-1 blockchain to Solana, including Readiness Working Group timing decisions in March-April 2023.
We propose a zero-trust validator deployment mechanism for proof-of-stake networks using NFTs and smart contracts. The approach enables programmable, transferable validator identity without trusting central authorities.
Applies NLP and bibliometric methods to analyze COVID-19 research output, identifying dominant topics, collaboration patterns, and temporal trends in the pandemic literature.
Examines employer knowledge and organizational images across novel labor market contexts. Investigates how brand signals and job seekers' knowledge structures interact to shape organizational attraction.
HR managers have a critical but underrecognized role in defending organizations against social engineering. This article argues that HR's people-facing position makes them essential for awareness, training, and policy.