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In smart grid, electricity consumption data may be handed over to a third party for various purposes. While government regulations and industry compliance prevent utility companies from improper or illegal sharing of their customers' electricity consumption data, there are some scenarios where it can be very useful. For example, it allows the consumers' data to be shared among various energy resources so the energy resources are able to analyze the data and adjust their operation to the actual power demand. However, it is crucial to protect sensitive electricity consumption data during the sharing process. In this paper, we propose a fine-grained access control scheme (FAC) with efficient attribute revocation and policy updating in smart grid. Specifically, by introducing the concept of Third-party Auditor (TPA), the proposed FAC achieves efficient attribute revocation. Also, we design an efficient policy updating algorithm by outsourcing the computational task to a cloud server. Moreover, we give security analysis and conduct experiments to demonstrate that the FAC is both secure and efficient compared with existing ABE-based approaches.

Vaccines need to be urgently allocated in pandemics like the ongoing COVID-19 pandemic. In the literature, vaccines are optimally allocated using various mathematical models, including the extensively used Susceptible-Infected-Recovered epidemic model. However, these models do not account for the time duration concerning multi-dose vaccines, time duration from infection to recovery or death, the vaccine hesitancy (i.e., delay in acceptance or refusal of vaccination), and vaccine efficacy (i.e., the time-varying protection capability of the vaccine). To make the vaccine allocation model more applicable to reality, this paper presents an optimal model considering the above mentioned time duration concerning multi-dose vaccination, time duration from infection to recovery or death, hesitancy rates, efficacy levels, and also breakthrough rates-the rates at which individuals get infected after vaccination. This vaccine allocation model is constructed using a revised Susceptible-Infected-Recovered model. The concept of people*week infections is introduced to measure the number of infected people within a certain time duration, and in this paper, the amount of people*week infections is minimized by the proposed vaccine allocation model. Our case study of the New York State 2021 population of 19,840,000 shows that this optimal allocation method can avoid 0.05%~2.75% people*week infections than the baseline allocation method when 2 to 11 million vaccines are optimally allocated. In conclusion, the obtained optimal allocation method can effectively reduce people*week infections and avoid vaccine waste when more vaccines are available.