ﻻ يوجد ملخص باللغة العربية
Collaborative inference has recently emerged as an intriguing framework for applying deep learning to Internet of Things (IoT) applications, which works by splitting a DNN model into two subpart models respectively on resource-constrained IoT devices and the cloud. Even though IoT applications raw input data is not directly exposed to the cloud in such framework, revealing the local-part models intermediate output still entails privacy risks. For mitigation of privacy risks, differential privacy could be adopted in principle. However, the practicality of differential privacy for collaborative inference under various conditions remains unclear. For example, it is unclear how the calibration of the privacy budget epsilon will affect the protection strength and model accuracy in presence of the state-of-the-art reconstruction attack targeting collaborative inference, and whether a good privacy-utility balance exists. In this paper, we provide the first systematic study to assess the effectiveness of differential privacy for protecting collaborative inference in presence of the reconstruction attack, through extensive empirical evaluations on various datasets. Our results show differential privacy can be used for collaborative inference when confronted with the reconstruction attack, with insights provided about privacyutility trade-offs. Specifically, across the evaluated datasets, we observe there exists a suitable privacy budget range (particularly 100<=epsilon<=200 in our evaluation) providing a good tradeoff between utility and privacy protection. Our key observation drawn from our study is that differential privacy tends to perform better in collaborative inference for datasets with smaller intraclass variations, which, to our knowledge, is the first easy-toadopt practical guideline.
In the Internet-of-Things, the number of connected devices is expected to be extremely huge, i.e., more than a couple of ten billion. It is however well-known that the security for the Internet-of-Things is still open problem. In particular, it is di
Privacy protection in electronic healthcare applications is an important consideration due to the sensitive nature of personal health data. Internet of Health Things (IoHT) networks have privacy requirements within a healthcare setting. However, thes
This paper is a general survey of all the security issues existing in the Internet of Things (IoT) along with an analysis of the privacy issues that an end-user may face as a consequence of the spread of IoT. The majority of the survey is focused on
Internet of Things (IoT) based applications face an increasing number of potential security risks, which need to be systematically assessed and addressed. Expert-based manual assessment of IoT security is a predominant approach, which is usually inef
User privacy concerns are widely regarded as a key obstacle to the success of modern smart cyber-physical systems. In this paper, we analyse, through an example, some of the requirements that future data collection architectures of these systems shou