Multi-party private set intersection (MPSI) allows 𝑛(𝑛 ≥ 3) participants, each holding a dataset of size 𝑚, to compute the intersection of their sets without revealing any additional information.We extract a primitive called bicentric zero-sharing, which can reduce MPSI to two-party PSI between two central participants named Pivot and Leader.We introduce an efficient instantiation of bicentric zero-sharing, which involves a round of sharing and reconstruction of an oblivious key-value store (OKVS) object.We then combine this construction with two-party PSI to propose a new efficient scalable MPSI protocol.We also propose protocols for computing MPSI variants based on bicentric zero-sharing, such as multi-party private set intersection cardinality (MPSI-CA) and multi-party threshold private set intersection (MTPSI).Our protocols are mainly based on symmetric-key operations, and the communication complexity of each participant is at most O (𝑛 + 𝑚).The security of our protocols relies on the assumption * The first two authors contribute equally.

This work studies optimal solar charging for solar-powered self-sustainable UAV communication networks, considering the day-scale time-variability of solar radiation and user service demand. The objective is to optimally trade off between the user coverage performance and the net energy loss of the network by proactively assigning UAVs to serve, charge, or land. Specifically, the studied problem is first formulated into a time-coupled mixed-integer non-convex optimization problem, and further decoupled into two sub-problems for tractability. To solve the challenge caused by time-coupling, deep reinforcement learning (DRL) algorithms are respectively designed for the two sub-problems. Particularly, a relaxation mechanism is put forward to overcome the “dimension curse” incurred by the large discrete action space in the second sub-problem. At last, simulation results demonstrate the efficacy of our designed DRL algorithms in trading off the communication performance against the net energy loss, and the impact of different parameters on the tradeoff performance.