Local Differential Privacy for Deep Learning (1908.02997v3)

Abstract: The internet of things (IoT) is transforming major industries including but not limited to healthcare, agriculture, finance, energy, and transportation. IoT platforms are continually improving with innovations such as the amalgamation of software-defined networks (SDN) and network function virtualization (NFV) in the edge-cloud interplay. Deep learning (DL) is becoming popular due to its remarkable accuracy when trained with a massive amount of data, such as generated by IoT. However, DL algorithms tend to leak privacy when trained on highly sensitive crowd-sourced data such as medical data. Existing privacy-preserving DL algorithms rely on the traditional server-centric approaches requiring high processing powers. We propose a new local differentially private (LDP) algorithm named LATENT that redesigns the training process. LATENT enables a data owner to add a randomization layer before data leave the data owners' devices and reach a potentially untrusted machine learning service. This feature is achieved by splitting the architecture of a convolutional neural network (CNN) into three layers: (1) convolutional module, (2) randomization module, and (3) fully connected module. Hence, the randomization module can operate as an NFV privacy preservation service in an SDN-controlled NFV, making LATENT more practical for IoT-driven cloud-based environments compared to existing approaches. The randomization module employs a newly proposed LDP protocol named utility enhancing randomization, which allows LATENT to maintain high utility compared to existing LDP protocols. Our experimental evaluation of LATENT on convolutional deep neural networks demonstrates excellent accuracy (e.g. 91%- 96%) with high model quality even under low privacy budgets (e.g. $\varepsilon=0.5$).

• The paper presents LATENT, a novel algorithm that integrates a randomization layer into CNNs to ensure local differential privacy and secure sensitive data during training.
• The method effectively preserves high model utility and accuracy on benchmark datasets like MNIST and CIFAR-10, even under strict privacy budgets.
• The approach enables practical, distributed deep learning in IoT, healthcare, and finance, eliminating the need for a trusted curator.

Enhancing Local Differential Privacy in Deep Learning with LATENT

Introduction to Local Differential Privacy in Deep Learning

The incursion of IoT in various sectors generates vast amounts of data continuously, necessitating advancements in deep learning (DL) technologies for efficient data processing and insights extraction. Deep learning models, especially convolutional neural networks (CNNs), have demonstrated exemplary performance in complex problem-solving areas like image classification and natural language processing. However, the utilisation of sensitive data for training DL models raises significant privacy concerns. Traditional machine learning approaches often expose private data during training, a risk exacerbated in DL due to the massive data requirements for model accuracy.

Addressing Privacy Concerns with LATENT

To tackle these privacy issues, this paper introduces a novel algorithm named LATENT that leverages local differential privacy (LDP) for the protection of sensitive information during the DL model training phase. LATENT ingeniously integrates a randomization layer within the CNN architecture, thus allowing data owners to perturb data locally before it is exposed to potentially untrusted environments for training. This approach effectively mitigates the risk of data leakage and privacy breaches.

The LATENT algorithm encapsulates the DL architecture into three distinct layers: a convolutional module for initial data processing, a novel randomization module for data perturbation ensuring LDP, and a fully connected module for final classification tasks. This structure not only enhances privacy but does so without significantly compromising the utility and accuracy of the learned models.

Experimental Validation and Implications

Extensive evaluations of LATENT on benchmark datasets like MNIST and CIFAR-10 demonstrate its effectiveness in preserving privacy without substantial accuracy loss, even under stringent privacy budgets (e.g., ε = 0.5). Compared to existing global differential privacy (GDP) approaches, LATENT offers improved practicality for IoT environments and cloud-based machine learning platforms by eliminating the need for a trusted curator and allowing efficient computation by data owners.

Moreover, LATENT's compatibility with software-defined networks (SDN) and network function virtualization (NFV) frameworks potentiates its application in distributed, edge-computing scenarios, further underscoring its relevance for future IoT models.

Engaging with LATENT's Potential

The development of LATENT marks a significant step towards reconciling the dual objectives of leveraging deep learning for big data analytics and ensuring stringent privacy protections. By facilitating locally perturbed training data, LATENT opens new pathways for privacy-preserving machine learning in sensitive applications, ranging from healthcare to finance.

Future explorations could extend LATENT's methodology to other forms of neural networks and delve into optimizing the trade-offs between privacy, accuracy, and computational efficiency. The adaptability of LATENT to different deep learning architectures and datasets promises a wide applicability, potentially setting a new standard for privacy-preserving machine learning practices.

Conclusion

In conclusion, LATENT represents a transformative approach to secure deep learning, effectively addressing the privacy-utility conundrum that plagues modern data analytics. Its innovative integration of local differential privacy within deep learning architectures paves the way for the safe and effective utilization of sensitive data, ensuring that the advancements in AI and machine learning are both powerful and privacy-preserving.