Evaluating the Efficacy of AI Techniques in Textual Anonymization: A Comparative Study
This addresses privacy concerns in textual data by evaluating incremental improvements in anonymization methods for digital applications.
The study compared AI techniques including CRF, LSTM, ELMo, and Transformers for text anonymization, finding that CRF, LSTM, and ELMo individually outperform traditional methods, with Transformers providing a broader perspective for optimal performance.
In the digital era, with escalating privacy concerns, it's imperative to devise robust strategies that protect private data while maintaining the intrinsic value of textual information. This research embarks on a comprehensive examination of text anonymisation methods, focusing on Conditional Random Fields (CRF), Long Short-Term Memory (LSTM), Embeddings from Language Models (ELMo), and the transformative capabilities of the Transformers architecture. Each model presents unique strengths since LSTM is modeling long-term dependencies, CRF captures dependencies among word sequences, ELMo delivers contextual word representations using deep bidirectional language models and Transformers introduce self-attention mechanisms that provide enhanced scalability. Our study is positioned as a comparative analysis of these models, emphasising their synergistic potential in addressing text anonymisation challenges. Preliminary results indicate that CRF, LSTM, and ELMo individually outperform traditional methods. The inclusion of Transformers, when compared alongside with the other models, offers a broader perspective on achieving optimal text anonymisation in contemporary settings.