In the current era, mobile cloud (MC) transactions raise concerns over the data stored in the MC. These data can be tampered with by third parties, leading to data loss and information misplacement. Such security brea...
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The increasing share of renewable generation leads to new challenges in reliable power system operation, such as the rising volatility of power generation, which leads to time-varying dynamics and behavior of the syst...
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The increasing share of renewable generation leads to new challenges in reliable power system operation, such as the rising volatility of power generation, which leads to time-varying dynamics and behavior of the system. To counteract the changing dynamics, we propose to adapt the parameters of existing controllers to the changing conditions. Doing so, however, is challenging, as large power systems often involve multiple subsystem operators, which, for safety and privacy reasons, do not want to exchange detailed information about their subsystems. Furthermore, centralized tuning of structured controllers for large-scale systems, such as power networks, is often computationally very challenging. For this reason, we present a hierarchical decentralized approach for controller tuning, which increases datasecurity and scalability. The proposed method is based on the exchange of structured reduced models of subsystems, which conserves data privacy and reduces computational complexity. For this purpose, suitable methods for model reduction and model matching are introduced. Furthermore, we demonstrate how increased renewable penetration leads to time-varying dynamics on the IEEE 68-bus power system, which underlines the importance of the problem. Then, we apply the proposed approach on simulation studies to show its effectiveness. As shown, similar system performance as with a centralized method can be obtained. Finally, we show the scalability of the approach on a large power system with more than 2500 states and about 1500 controller parameters.
This paper introduces an authentic framework that relies on verifiable searchable encryption security and a mechanism for managing resource limitations on the client-side. The framework is specifically designed to tac...
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ISBN:
(纸本)9798350385328;9798350385335
This paper introduces an authentic framework that relies on verifiable searchable encryption security and a mechanism for managing resource limitations on the client-side. The framework is specifically designed to tackle the difficulty of achieving a high level of datasecurity while minimizing the storage requirements on the client-side. The framework combines a symmetric key encryption technique with a Searchable Encryption Index (SEI) to allow for efficient searches while maintaining data secrecy. One important aspect is the utilization of cryptographic verification tokens, which enable clients to verify the integrity and validity of search results while keeping a consistently small storage space requirement. This is accomplished by exclusively storing crucial keys and tokens on the client's end. Our framework incorporates advanced security measures such as key rotation, auditing systems, and end-to-end encryption. This ensures a strong and efficient solution for safe data searches, making it especially suitable for contexts with limited client-side resources. The practical usefulness of searchable encryption is validated through rigorous testing and third-party audits, ensuring a balance between security, efficiency, and scalability.
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