Distribution grid agents are obliged to exchange and disclose their states explicitly neighboring regions enable distributed optimal power flow dispatch. However, the contain sensitive information of individual agents, such as voltage current measurements. These measurements can be inferred by adversaries, other participating or eavesdroppers, leading privacy leakage problem. To address issue, ...

Partial orders and directed acyclic graphs are commonly recurring data structures that arise naturally in numerous domains applications used to represent ordered relations between entities the domains. Examples task dependencies a project plan, transaction order distributed ledgers execution sequences of tasks computer programs, just mention few. We study problem preserving hierarchical cluster...

Abstract Classical machine learning modeling demands considerable computing power for internal calculations and training with big data in a reasonable amount of time. In recent years, clouds provide services to facilitate this process, but it introduces new security threats breaches. Modern encryption techniques ensure are considered as the best option protect stored transit from an unauthorize...

With the advance of machine learning and Internet Things (IoT), security privacy have become critical concerns in mobile services networks. Transferring data to a central unit violates sensitive data. Federated mitigates this need transfer local by sharing model updates only. However, leakage remains an issue. This paper proposes xMK-CKKS, improved version MK-CKKS multi-key homomorphic encrypti...

Privacy protection has been an important concern with the great success of machine learning. In this paper, it proposes a multi-party privacy preserving learning framework, named PFMLP, based on partially homomorphic encryption and federated The core idea is all parties just transmitting encrypted gradients by encryption. From experiments, model trained PFMLP almost same accuracy, deviation les...