Threat Intelligence Driven IP Protection for Entrepreneurial SMEs

Entrepreneurial small to medium enterprises face significant cybersecurity challenges when developing valuable intellectual property IP. This paper addresses the critical gap in research on how E-SMEs can protect their IP assets from cybersecurity threats through effective threat intelligence and...

Retrofit: Continual Learning with Bounded Forgetting for Security Applications

Modern security analytics are increasingly powered by deep learning models, but their performance often degrades as threat landscapes evolve and data representations shift. While continual learning CL offers a promising paradigm to maintain model effectiveness, many approaches rely on full...

Correlating Account on Ethereum Mixing Service Via Domain-Invariant Feature Learning

The untraceability of transactions facilitated by Ethereum mixing services like Tornado Cash poses significant challenges to blockchain security and financial regulation. Existing methods for correlating mixing accounts suffer from limited labeled data and vulnerability to noisy annotations, whic...

A Survey on Privacy Risks and Protection in Large Language Models

Although Large Language Models LLMs have become increasingly integral to diverse applications, their capabilities raise significant privacy concerns. This survey offers a comprehensive overview of privacy risks associated with LLMs and examines current solutions to mitigate these challenges. Firs...

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation

Large Language Models LLM are typically trained on vast amounts of data from various sources. Even when designed modularly e.g., Mixture-of-Experts, LLMs can leak privacy on their sources. Conversely, training such models in isolation arguably prohibits generalization. To this end, we propose a...