Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Masking and Demasking Agents

Masking and Demasking Agents

EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

Improving Translational Accuracy

Improving Translational Accuracy

Language Development

Language Development

Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...

Language and Cognition

Language and Cognition

Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Reaction mechanisms and microstructural development of MSWI fly ash in geopolymers enhanced by mechanochemical activation.

Waste management (New York, N.Y.)·2026

Same author

Influence of Aldehyde-Based Modifiers on Rubber Asphalt: Properties, Deodorization Effect, and Mechanistic Analysis.

Polymers·2026

Same author

Vacancy-induced Fermi level shifts for heterojunction reconstruction boosting photocatalytic hydrogen evolution.

Journal of colloid and interface science·2026

Same author

TAGNN: topology-aware graph neural network framework for link prediction.

Scientific reports·2026

Same author

Epigallocatechin-3-Gallate (EGCG) Mitigates Deep Vein Thrombosis Through Antioxidant and Anti-Ferroptotic Actions via the NRF2-NEDD4 Pathway.

Phytotherapy research : PTR·2026

Same author

Targeted therapies in lung cancer: personalizing treatment across the age spectrum.

Frontiers in oncology·2026

Search research articles

Related Experiment Video

Updated: Jan 17, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Exploiting Privacy Preserving Prompt Techniques for Online Large Language Model Usage.

Youxiang Zhu¹, Ning Gao¹, Xiaohui Liang¹

¹Department of Computer Science, University of Massachusetts Boston, MA, USA.

... IEEE Global Communications Conference. IEEE Global Communications Conference

|September 18, 2025

Summary

This summary is machine-generated.

A new local privacy-preserving prompt assistant (LPPA) helps users protect sensitive information in prompts for online Large Language Models (LLMs). The LPPA modifies prompts to safeguard privacy while maintaining LLM output utility.

Keywords:

Privacy-preserving techniques large language models online and offline

More Related Videos

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Related Experiment Videos

Last Updated: Jan 17, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

Computer Science
Artificial Intelligence
Cybersecurity

Background:

Online Large Language Models (LLMs) are increasingly used for sensitive tasks like financial advice.
Direct prompt submission to LLM servers risks exposing private data and enabling user profiling.
Existing methods lack effective user-controlled privacy-preserving mechanisms for LLM interactions.

Purpose of the Study:

To introduce a local privacy-preserving prompt assistant (LPPA) for balancing prompt privacy and LLM output utility.
To develop methods for identifying and mitigating sensitive keywords within user prompts.
To enable users to safeguard sensitive information without compromising the usefulness of LLM-generated content.

Main Methods:

Proposed a privacy module to detect sensitive keywords in prompts.
Implemented four privacy techniques: remove, mask, replace, and rewrite for keyword protection.
Developed a utility inference model to predict the impact of prompt modifications on LLM output quality locally.
Evaluated the LPPA system using real-world user prompts.

Main Results:

The 'remove' technique demonstrated the highest performance in preserving privacy.
The LPPA effectively identifies sensitive keywords and suggests prompt modifications.
The utility inference model accurately predicts the impact of prompt changes on LLM output.
Users can adjust prompts to enhance privacy while retaining satisfactory LLM utility.

Conclusions:

The local privacy-preserving prompt assistant (LPPA) offers a practical solution for enhancing user privacy with online LLMs.
Prompt modification techniques, particularly removal, are effective in protecting sensitive data.
LPPA empowers users to control their data privacy without significant loss of LLM functionality.