相关论文: Big Data Privacy Context: Literature Effects On Se…

The use of language technologies in high-stake settings is increasing in recent years, mostly motivated by the success of Large Language Models (LLMs). However, despite the great performance of LLMs, they are are susceptible to ethical…

Recent advancements in pre-trained large language models (LLMs) have significantly influenced various domains. Adapting these models for specific tasks often involves fine-tuning (FT) with private, domain-specific data. However, privacy…

[Context] Systematic Literature Review (SLR) has been a major type of study published in Software Engineering (SE) venues for about two decades. However, there is a lack of understanding of whether an SLR is really needed in comparison to a…

Through academic publications, the authors of these publications form a social network. Instead of sharing casual thoughts and photos (as in Facebook), authors pick co-authors and reference papers written by other authors. Thanks to various…

Large Language Models (LLMs) are increasingly deployed in mental health contexts, from structured therapeutic support tools to informal chat-based well-being assistants. While these systems increase accessibility, scalability, and…

The widespread use of preprint repositories such as arXiv has accelerated the communication of scientific results but also introduced overlooked security risks. Beyond PDFs, these platforms provide unrestricted access to original source…

The rapid development of large language models (LLMs) has yielded impressive success in various downstream tasks. However, the vast potential and remarkable capabilities of LLMs also raise new security and privacy concerns if they are…

Large language models (LLMs) are rapidly being adopted for tasks like drafting emails, summarizing meetings, and answering health questions. In these settings, users may need to share private information (e.g., contact details, health…

When users submit queries to Large Language Models (LLMs), their prompts can often contain sensitive data, forcing a difficult choice: Send the query to a powerful proprietary LLM providers to achieving state-of-the-art performance and risk…

Citation counts remain the dominant metric for assessing research impact, yet they suffer from well-documented limitations: temporal lag, disciplinary bias, and Matthew effects. Here we propose LLM-Metrics, a research-impact assessment…

Academic institutions, federal agencies, publishers, editors, authors, and librarians increasingly rely on citation analysis for making hiring, promotion, tenure, funding, and/or reviewer and journal evaluation and selection decisions. The…

Large Language Models (LLMs) have demonstrated advanced capabilities in both text generation and comprehension, and their application to data archives might facilitate the privatization of sensitive information about the data subjects. In…

The eruption of big data with the increasing collection and processing of vast volumes and variety of data have led to breakthrough discoveries and innovation in science, engineering, medicine, commerce, criminal justice, and national…

Recent work~\cite{Liu2016} has shown that dependencies between items in a dataset can lead to privacy leaks. We extend this concept to privacy-preserving transformations, considering a broader set of dependencies captured by correlation…

Technology is shaping our lives in a multitude of ways. This is fuelled by a technology infrastructure, both legacy and state of the art, composed of a heterogeneous group of hardware, software, services and organisations. Such…

Policies ensuring that research data are available on public archives are increasingly being implemented at the government [1], funding agency [2-4], and journal [5,6] level. These policies are predicated on the idea that authors are poor…

This paper investigates the performance of several representative large models in the field of literature recommendation and explores potential biases. The results indicate that while some large models' recommendations can be somewhat…

Researchers are increasingly exploring how gender, culture, and other sociodemographic factors correlate with user computer security and privacy behaviors. To more holistically understand relationships between these factors and behaviors,…

The imposing evolution of artificial intelligence systems and, specifically, of Large Language Models (LLM) makes it necessary to carry out assessments of their level of risk and the impact they may have in the area of privacy, personal…

Purpose: We seek to explore the realm of literature about digital libraries. We specifically seek to ascertain how interest in this subject has evolved, its impact, the most productive journals and countries, the number of occurrences of…