Large language models (LLMs) are increasingly deployed, yet they introduce significant privacy risks by disclosing personally identifiable information (PII) during interactions. Although prior work has demonstrated the feasibility of extracting PII from LLMs, no comprehensive study has evaluated the actual extent of PII leakage across mainstream LLMs or investigated user perceptions, literacy, and behavioral responses to these risks. To address these gaps, we conduct a large-scale evaluation of PII leakage in popular LLMs, demonstrating that attackers can extract email addresses and phone numbers with high success rates. Through a mixed-methods study involving 20 interviews and 204 survey participants, we identify significant discrepancies between user concerns and behavior: despite strong concerns about PII leakage and limited understanding of training data provenance, users continue to use LLMs due to perceived utility, often exhibiting privacy cynicism. Based on these findings, we propose design implications for enhancing the privacy-utility balance in future LLM deployments.

Large Language Models (LLMs) have demonstrated exceptional success across a variety of tasks, particularly in natural language processing, leading to their growing integration into numerous facets of daily life. However, this widespread deployment has raised substantial privacy concerns, especially regarding personally identifiable information (PII), which can be directly associated with specific individuals. The leakage of such information presents significant real-world privacy threats. In this paper, we conduct a systematic investigation into existing research on PII leakage in LLMs, encompassing commonly utilized PII datasets, evaluation metrics, and current studies on both PII leakage attacks and defensive strategies. Finally, we identify unresolved challenges in the current research landscape and suggest future research directions.

In this article, a rugged linear array is proposed for covering both the LTE and 5G bands with an intermediate gain. The antenna is composed of a driven element, a set of directors, and a set of reflectors, where the excited element is a wideband high-efficiency electromagnetic structure (WHEMS) and the parasitic elements consist of metal rods. To achieve a rugged design, similar to the classic Yagi antenna, all of the elements should be conductively connected, so that it can be welded. The weldable mechanism is started on the driven radiating element. In addition, a balun is introduced in the antenna to reduce the influence of unbalanced common-mode currents. A wind resistance analysis is also presented, where the drag force of the proposed antenna is approximately a quarter of that for an antenna with a metal plate. The antenna exhibits a gain of 10.8-13.3 dBi for a 78% fractional bandwidth (1.7-3.7 GHz), which is a sevenfold increase from that of the Yagi antenna, without sacrificing the gain or rugged design. The proposed antenna has the advantages of a simple feeding arrangement, low cost, lightweight, low-wind resistance, and rugged structure; and is suitable for all-weather large-scale Internet-of-Things (IoT) deployment at a rural site or in a harsh networking environment.