A Partitioned Task Offloading Approach for Privacy Preservation at Edge

Internet of Things (IoT) refers to the network of physical objects connected over the Internet for data collection and sharing which in turn has enormous positive impacts in various fields like health care, governance, and manufacturing. However, privacy threats have emerged as the downside of IoT due to the involvement of sensitive information. Most recent approaches have proposed adversarial training to enhance privacy. But the assumption of an adversary during the training is not likely to handle real-world threats. To handle the issue, we propose a Deep Neural Network (DNN)-based approach which is employed at the edge. A partitioned Denoising Autoencoder (DAE) is deployed at the edge where data is collected thus making it burdensome for the adversary to infer any information since the model is partitioned. The accuracy of the adversary reidentifying sensitive information could not exceed 42.60%, while the digit recognition achieved 96.59%. Experiments are conducted on a Handwritten digit recognition dataset, and the proposed technique does not leverage heavy computation like state-of-the-art techniques.