SecureLLM: A Unified Framework for Privacy-Focused Large Language Models
Source abstract
Abstract
Large language models (LLMs) have shown remarkable skills across various activities, including text generation and code synthesis. Their widespread applicability, however, raises substantial concerns about security, privacy, and possibly misuse. Of recent legislative efforts, the most notable is the proposed EU AI Act, which classifies specific AI applications as high-risk. For detailed regulatory guidance, also refer to the GDPR and HIPAA privacy rules. This paper introduces SecureLLM, a novel framework that integrates lightweight cryptographic protocols, decentralized fine-tuning strategies, and differential privacy to mitigate data leakage and adversarial attacks in LLM ecosystems. We propose SecureLLM as a conceptual security architecture for LLMs, offering a unified approach that can be adapted and tested in real-world deployments. While extensive empirical benchmarks are deferred to future studies, we include a small-scale demonstration illustrating how differential privacy can reduce membership inference risks with a manageable overhead. The SecureLLM framework underscores the potential of cryptography, differential privacy, and decentralized fine-tuning for creating safer and more compliant AI systems.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Privacy-Preserving Technologies in Data
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Informatics & Telematics, Harokopio University of Athens, 17676 Kallithea, Greece
Independent Researcher, 11474 Athens, Greece
School of Science and Technology, Hellenic Open University, 26331 Patras, Greece
References (65)
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