ABSTRACT
More than 6 million people have lost their lives due to COVID-19 across the world (Ghatkopar in Fake negative COVID-19 certificate scam unearthed, 2019, [2];WHO (World Health Organization) in https://covid19.who.int/table, [3]). Recently, fake COVID-19 test certificate scams have spiked up drastically and become one of the reasons for the spread of COVID-19. In light of the current scenario, this paper proposes a decentralized approach called, "D-Test” for COVID-19 testing which allows the hospital and the general public to register themselves at a common platform which follows the concept of CIA triad (Confidentiality, Integrity, and Availability) and allows users to register without any fear of data breach. This platform registers users based on smart contract and enables the user to do the following once registered successfully: (a) Book Testing Slot, (b) Find nearby registered testing laboratories, and c) Generate the COVID-19 reports which could be imported and exported as and when required by the user. This has a higher value of trust because the source of the report can be traced back since usage of Blockchain prevents the likelihood of data tampering by an entity. This framework could help the government(s) keep track of distributing authentic COVID-19 testing certificates, prevent the fake COVID-19 testing certificate scams, and will speed up the process of verifying the users' test reports, thereby saving lives of many citizens around the world. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Several countries adopted the Google & Apple exposure notification system (GAEN) to slow the spread of the SARS-CoV-2 virus down. GAEN promised to guarantee security and privacy through a decentralized approach. In this paper, we report several relevant privacy and integrity threats in GAEN, including new attacks. GAEN's security issues are not inherent risks of contact tracing systems. Indeed, we also propose a system named Pronto-B2 which enjoys a much better resilience with respect to mass surveillance and replay attacks. IEEE
ABSTRACT
Shifting the paradigm to decarbonized, distributed renewable future implies changes to conventional principles of power systems operation and requires the implementation of smart grid concepts. Microgrids have been widely recognized as a decentralized approach to successfully integrating renewable energy sources and end consumer empowerment. However, their implementation requires significant improvements and transformation of the distribution system in terms of increased observability and controllability, especially in the context of (near) real-time operation. Supervisory, Control, and Data Acquisition Systems (SCADA) enable system and infrastructure automated monitoring and control and serve as a foundation for advanced management and application of optimization-driven operation. Moreover, the development and testing of the functions mentioned above is a complex task, and today there is still a lack of holistic simulation tools, even though well-established power system simulators exist. The main objective of this paper is to introduce a novel simulation tool developed to simulate the SCADA system used in the Smart Grid Laboratory of the Faculty of Electrical Engineering and Computing for control, integration, and interactions between a microgrid's components. This paper includes simulator system architecture design, implemented functionalities, and future directions. Simulator testing shows successful communication, measurement generation, and meaningful response to commands and reference signals, proving correct functionality. Besides significant value in testing SCADA functionality, designing such a simulator has been of great benefit during restricted access to real-world devices in the Smart Grid Laboratory during the COVID-19 pandemic lockdown. © 2022 Croatian Society MIPRO.
ABSTRACT
In light of the COVID-19 pandemic, the need for a chest X-ray scans classifier is crucial in order to diagnose patients and classify scans into normal, COVID-infected, and pneumonia. Federated learning was chosen for the classification as it uses a decentralized approach to train the model at the local servers belonging to each entity in various geographic locations. Therefore, information leakage that could happen from the traditional centralized approach of training is prevented, besides saving the huge cost of central storage. However, between the vast difference in the number of X-ray scans per data-silo (i.e. hospital), the dissimilar image-Acquisition techniques, and the diverse morphological structures of the human chest, non-IID (non-Independent and Identically Distributed) skews are introduced in the data. In this paper, real-world datasets of COVID and pneumonia scans are used to satisfy all the non-IID data skews. An experiment was then conducted to test the effect of these skews using five federated learning algorithms, FedAvg, FedProx, FedNova, SCAFFOLD, and FedBN, under the same metrics. The obtained accuracy values are 79.5%, 76.92%, 5.57%, 79.18%, and 84.4%, respectively. In this paper, we present the different effects of non-IID skews on the training process and discuss the different federated learning variations to mitigate the data heterogeneity. © 2022 IEEE.
ABSTRACT
Multisite medical data sharing is critical in modern clinical practice and medical research. The challenge is to conduct data sharing that preserves individual privacy and data utility. The shortcomings of traditional privacy-enhancing technologies mean that institutions rely upon bespoke data sharing contracts. The lengthy process and administration induced by these contracts increases the inefficiency of data sharing and may disincentivize important clinical treatment and medical research. This paper provides a synthesis between 2 novel advanced privacy-enhancing technologies-homomorphic encryption and secure multiparty computation (defined together as multiparty homomorphic encryption). These privacy-enhancing technologies provide a mathematical guarantee of privacy, with multiparty homomorphic encryption providing a performance advantage over separately using homomorphic encryption or secure multiparty computation. We argue multiparty homomorphic encryption fulfills legal requirements for medical data sharing under the European Union's General Data Protection Regulation which has set a global benchmark for data protection. Specifically, the data processed and shared using multiparty homomorphic encryption can be considered anonymized data. We explain how multiparty homomorphic encryption can reduce the reliance upon customized contractual measures between institutions. The proposed approach can accelerate the pace of medical research while offering additional incentives for health care and research institutes to employ common data interoperability standards.