Trusted third party

About: Trusted third party is a research topic. Over the lifetime, 2919 publications have been published within this topic receiving 60935 citations.

Trusted Third Party Based Key Management for Enhancing LoRaWAN Security

Abstract: The Internet of things (IoT) is pervading our lives to allow a comfortable and smarter human living space by connecting surrounding human things to the Internet. But, it reveals sensitive and private data as well as human appliances to intruders. To solve these problems, security solutions based on cryptography might be used. But, these solutions are based on encryption keys that must be managed securely and properly while taking into account the IoT characteristics. In this paper, we investigate the LoRaWAN IoT architecture. We evaluate its security by gathering possible attacks that can be launched on it, using the Scyther tool. Then, we propose an enhanced version of the LoRaWAN architecture that solves those attacks. We evaluate the proposed solution in terms of security and key management requirements and compare it to existing solutions.

Privacy-Preserving Asynchronous Federated Learning Algorithms for Multi-Party Vertically Collaborative Learning.

Abstract: The privacy-preserving federated learning for vertically partitioned data has shown promising results as the solution of the emerging multi-party joint modeling application, in which the data holders (such as government branches, private finance and e-business companies) collaborate throughout the learning process rather than relying on a trusted third party to hold data. However, existing federated learning algorithms for vertically partitioned data are limited to synchronous computation. To improve the efficiency when the unbalanced computation/communication resources are common among the parties in the federated learning system, it is essential to develop asynchronous training algorithms for vertically partitioned data while keeping the data privacy. In this paper, we propose an asynchronous federated SGD (AFSGD-VP) algorithm and its SVRG and SAGA variants on the vertically partitioned data. Moreover, we provide the convergence analyses of AFSGD-VP and its SVRG and SAGA variants under the condition of strong convexity. We also discuss their model privacy, data privacy, computational complexities and communication costs. To the best of our knowledge, AFSGD-VP and its SVRG and SAGA variants are the first asynchronous federated learning algorithms for vertically partitioned data. Extensive experimental results on a variety of vertically partitioned datasets not only verify the theoretical results of AFSGD-VP and its SVRG and SAGA variants, but also show that our algorithms have much higher efficiency than the corresponding synchronous algorithms.

Contract-based cloud architecture

Abstract: Cloud Computing as a service on demand architecture has become a topic of interest in the last few years. The outsourcing of duties and infrastructure to external parties enables new services to be established quickly and with low financial risk. These services also can be scaled on demand. Nevertheless, several issues such as security and legality should be considered before entering the cloud. The financial benefits of cloud services conflict with the need to secure and control the access to outsourced information. Companies have to comply with diverse laws across jurisdictions and are accountable to various national regulators. Security requirements may not be compatible with those offered by existing providers. In this paper, we propose an architecture to facilitate the integration of these security requirements in the cloud environment and to address the legal issues attached. Our approach customizes the selection of a service provider based on the companies preference. We also define a trusted third party to handle the monitoring and auditing processes over different service providers.