A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics (1703.02952v7)

Abstract: Internet of Things (IoT) devices and applications are being deployed in our homes and workplaces. These devices often rely on continuous data collection to feed machine learning models. However, this approach introduces several privacy and efficiency challenges, as the service operator can perform unwanted inferences on the available data. Recently, advances in edge processing have paved the way for more efficient, and private, data processing at the source for simple tasks and lighter models, though they remain a challenge for larger, and more complicated models. In this paper, we present a hybrid approach for breaking down large, complex deep neural networks for cooperative, privacy-preserving analytics. To this end, instead of performing the whole operation on the cloud, we let an IoT device to run the initial layers of the neural network, and then send the output to the cloud to feed the remaining layers and produce the final result. In order to ensure that the user's device contains no extra information except what is necessary for the main task and preventing any secondary inference on the data, we introduce Siamese fine-tuning. We evaluate the privacy benefits of this approach based on the information exposed to the cloud service. We also assess the local inference cost of different layers on a modern handset. Our evaluations show that by using Siamese fine-tuning and at a small processing cost, we can greatly reduce the level of unnecessary, potentially sensitive information in the personal data, and thus achieving the desired trade-off between utility, privacy, and performance.

• The paper introduces a novel hybrid framework that processes data on-device to minimize sensitive data transfer to the cloud.
• It implements Siamese fine-tuning and layer separation to effectively preserve privacy in tasks like gender classification and activity recognition.
• Experimental evaluations show a balanced trade-off between computational efficiency and privacy, laying groundwork for future AI systems.

A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics

The paper presents a novel hybrid deep learning architecture designed to enhance privacy in mobile analytics. This research addresses the increasing demand for privacy-preserving practices in the integration of IoT devices and mobile data collection with cloud-based machine learning services. A typical approach where raw data is offloaded to the cloud raises profound privacy concerns and resource inefficiencies. The proposed solution seeks to intervene in this process by restructuring the data handling mechanism.

The key contribution of the paper is the introduction of a hybrid deep learning architecture wherein initial layers of a deep neural network process data locally on users' devices. This approach minimizes the amount of sensitive data transferred to the cloud by sending only processed outputs, which cannot be used for unintended inferences. The subsequent layers residing on the cloud continue processing this reduced-information output to execute the primary analytics task. The implementation of this framework uses advanced techniques, one of which is Siamese fine-tuning. This method is employed to ensure that the features extracted by early layers focus solely on the task at hand, insulating against the potential abusive inference of sensitive information.

In evaluating this hybrid architecture, the authors conduct experiments using two concrete tasks: gender classification through image data and activity recognition via sensor data. Numerical results demonstrate the hybrid model's efficacy in preserving privacy while providing sufficient data analytics utility. Furthermore, the evaluations explore various configurations of layer separation in deep CNNs, such as the VGG-16 network for gender classification, emphasizing the balance between computational load and data privacy.

The paper also introduces different methods to verify and evaluate the level of privacy retained by the model, such as transfer learning and deep visualization. Transfer learning was used to showcase how feature separability influences privacy, with results showing decreased secondary task accuracy—indicative of effective privacy measures. Moreover, the deep visualization approach presents a novel way to ascertain the inferable information content in intermediate representations, adding a layer of qualitative evaluation to privacy.

Implications of this research are multifaceted. Practically, it suggests a viable pathway for deploying efficient, privacy-conscious AI services on resource-limited devices without sacrificing model performance significantly. Theoretically, it proposes a systematic approach to layer separation that can act as a blueprint for future privacy-preserving architectures in AI systems.

In terms of future developments, the paper identifies potential enhancements for broader applications, such as extending the framework to recurrent neural networks for handling sequential data and improving hyper-parameter optimization for balancing the utility and privacy trade-off.

This work contributes significantly to the evolving discourse on privacy-preserving machine learning, setting a cornerstone for deploying intelligence at the edge, safeguarding user privacy without negating the analytical benefits provided by cloud-based AI services.