4 citations · 6 across the 10 of their papers we have counts for
11 papers
When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models
Byoungjae Min, Kennedy Edemacu, Sae-Hong Cho +3
Large language models (LLMs) are often asked whether something is absent from a record, list, or retrieved context. Yet non-observation licenses a negative answer only when evidenc…
DP-MemView: A Memory Interface for Attribute-Level Transcript Privacy in Long-Term LLM Agents
Jong Wook Kim, Byoungjae Min, Kennedy Edemacu +3
Long-term memory enables persistent personalization in LLM agents, but repeated memory-conditioned responses can cumulatively reveal protected attributes even when they are never s…
Privacy Without Losing Place: A Paradigm for Private Retrieval in Spatial RAGs
Kennedy Edemacu, Mohammad Mahdi Shokri, Vinay M. Shashidhar +1
This work introduces PAS -- Privacy Anchor Substitution, a structured mechanism for enabling user location privacy in spatial retrieval-augmented generation (RAG) systems. Unlike c…
Hidden in the Metadata: Stealth Poisoning Attacks on Multimodal Retrieval-Augmented Generation
Kennedy Edemacu, Mohammad Mahdi Shokri
Retrieval-augmented generation (RAG) has emerged as a powerful paradigm for enhancing multimodal large language models by grounding their responses in external, factual knowledge a…
Defending Against Knowledge Poisoning Attacks During Retrieval-Augmented Generation
Kennedy Edemacu, Vinay M. Shashidhar, Micheal Tuape +3
Retrieval-Augmented Generation (RAG) has emerged as a powerful approach to boost the capabilities of large language models (LLMs) by incorporating external, up-to-date knowledge so…
Fair In-Context Learning via Latent Concept Variables
Karuna Bhaila, Minh-Hao Van, Kennedy Edemacu +3
The emerging in-context learning (ICL) ability of large language models (LLMs) has prompted their use for predictive tasks in various domains with different data types, including t…
Privacy Preserving Prompt Engineering: A Survey
Kennedy Edemacu, Xintao Wu
Pre-trained language models (PLMs) have demonstrated significant proficiency in solving a wide range of general natural language processing (NLP) tasks. Researchers have observed a…
DP-TabICL: In-Context Learning with Differentially Private Tabular Data
Alycia N. Carey, Karuna Bhaila, Kennedy Edemacu +1
In-context learning (ICL) enables large language models (LLMs) to adapt to new tasks by conditioning on demonstrations of question-answer pairs and it has been shown to have compar…
Scalable Multi-Party Privacy-Preserving Gradient Tree Boosting over Vertically Partitioned Dataset with Outsourced Computations
Kennedy Edemacu, Beakcheol Jang, Jong Wook Kim
Due to privacy concerns, multi-party gradient tree boosting algorithms have become widely popular amongst machine learning researchers and practitioners. However, limited existing…
Reliability Check via Weight Similarity in Privacy-Preserving Multi-Party Machine Learning
Kennedy Edemacu, Beakcheol Jang, Jong Wook Kim
Multi-party machine learning is a paradigm in which multiple participants collaboratively train a machine learning model to achieve a common learning objective without sharing thei…
Packet Drop Attack Detection Techniques in Wireless Ad hoc Networks: A Review
Kennedy Edemacu, Martin Euku, Richard Ssekibuule
Wireless ad hoc networks have gained lots of attention due to their ease and low cost of deployment. This has made ad hoc networks of great importance in numerous military and civi…