Homomorphic Encryption and Dynamic Sparse Attention for Advancing Privacy in Dialogue Models

By Dr Silpaja Chandrasekar, PhDReviewed by Susha Cheriyedath, M.Sc.Dec 13 2023

In a paper published in the journal Applied Sciences, researchers introduced an inventive privacy-preserving dialogue model framework designed to address the challenges of safeguarding personal privacy in the information era. This framework seamlessly integrates Fully Homomorphic Encryption (FHE) technology with dynamic sparse attention (DSA) mechanisms, aiming to elevate the efficiency and accuracy of dialogue systems while upholding user privacy.

Experimental comparative analyses have validated the advantages of this framework, highlighting significant improvements in precision, recall, accuracy, and latency. Notably, the newly proposed DSA module, ensuring data security, has demonstrated a remarkable performance enhancement compared to traditional multi-head attention mechanisms, improving up to 100 times.

Background

In this era of evolving artificial intelligence (AI), conversational large models have gained prominence across various sectors but raised concerns about user data privacy. Past studies showcased their potential while highlighting privacy and accuracy issues. Protecting personal data in these systems is crucial, leading to various privacy models, including FHE. However, integrating FHE into conversational models faces hurdles due to computational inefficiencies.

Privacy-Preserving Dialogue Model Framework Overview

Developing a privacy-preserving dialogue model framework rooted in FHE and attention mechanisms begins with a meticulous dataset collection, pivotal for training and evaluating the model. A strategic approach is adopted, encompassing public datasets like the Cornell Movie-Dialogs Corpus, Ubuntu Dialogue Corpus, Stanford Question Answering Dataset (SQuAD), Twitter Customer Support Dataset, and Medical Dialogue Dataset.

Each dataset serves distinct purposes, testing the model's capabilities across various conversational domains, from everyday dialogues to technical support and medical consultations. Complementing these, synthetic data generated through natural language processing techniques enriches the diversity of dialogue patterns, enhancing the model's adaptability.

The subsequent step, dataset preprocessing, is a crucial bridge between raw data and model comprehension. This transformative stage involves several pivotal phases: data cleaning to eliminate noise and irrelevant information, text normalization for uniformity, tokenization to process text into meaningful units, vocabulary construction to assign unique indices to words, text encoding for numerical transformation, and sequence padding/truncation for consistent sequence length. An additional step ensures compatibility with encryption operations, particularly in this study's homomorphic encryption context.

Mathematically grounded principles underscore this preprocessing, notably in text encoding and sequence handling. Word embeddings, represented through an embedding matrix, convert discrete words into continuous vector points, capturing semantic relationships. Sequence padding or truncation adjusts sequence lengths uniformly. This meticulous preprocessing impacts model training efficiency, stability, and privacy protection.

The research highlights the criticality of preprocessing in safeguarding user privacy and facilitating efficient model training. By cleansing noise and standardizing data, it fortifies model quality, aids computational efficiency, and ensures compatibility with homomorphic encryption. The ensuing sections delve into the application of pre-processed data in the proposed homomorphic encryption-based dialogue model, examining performance and practical applications.

The method introduced revolves around a novel dialogue model framework merging FHE with a DSA mechanism. The primary goal lies in enabling complex natural language processing tasks on encrypted data without compromising data privacy. It entails replacing critical operators in the transformer architecture with their homomorphic encryption-compatible versions. The framework's design ensures robust privacy protection while minimizing the impact on model performance. This method is not restricted to dialogue systems alone but extends to other language processing tasks demanding privacy protection.

The subsequent focus delves into the specificities of the privacy-preserving transformer framework and its distinction from the original transformer model. Essential modifications involve adjustments to the self-attention mechanism, encryption of weights and biases, and safeguarding intermediate states—all ensuring data security at every computational step. The processing flow, from encrypted input to output, ensures data remains encrypted throughout, only decrypted locally by the user.

Moreover, integrating a DSA module emerges as a pivotal strategy to reduce computational intensity while preserving the essence of the attention mechanism. This module selectively prioritizes crucial sequence information in addressing the computational challenges inherent in homomorphic encryption environments. Its design optimizes computational efficiency, critical to handling large-scale natural language tasks within the encrypted domain.

Experimental evaluation metrics encompass accuracy, response time, and computational efficiency—critical markers for assessing model practicality, efficiency, and security. Precision, recall, accuracy, and latency are quantifiable measures for evaluating the proposed model against five baseline models. The choice of optimizer, hyperparameters, and strategic division of dataset parts ensures the model's generalization capability. At the same time, comparing different privacy protection technologies highlights the unique advantages of the proposed framework.

Model Comparison and Latency Optimization

Extensive metrics compare various natural language processing (NLP) models concerning privacy, performance, and efficiency. The analysis covers the transformer model, FHE, the proposed method, Secure Outsourced Textual EntitY Recognition (SOTER), Trusted Execution Environment (TEE), and Differential Privacy (DP), highlighting strengths and weaknesses. It explores latency aspects—computation, communication, and encryption—to optimize and ensure privacy.

Empirical evidence supports the proposed method's efficiency, showing minimal latency increase compared to unencrypted models and outperforming FHE. The DSA module's ablation study also demonstrates its superiority, enhancing performance and security in NLP tasks, particularly in precision and recall scenarios, while preserving user privacy.

Conclusion

To summarize, the study introduces a privacy-preserving framework for dialogue-based models, ensuring user privacy without compromising system performance. It employs innovative encryption, controls latency effectively, and enhances precision over traditional methods. As this work sets a new path for secure dialogue models, outlining future directions for improved security and AI development, future research aims at refining encryption and exploring broader applications.