Multi-party private set intersection (PSI) enables multiple parties to compute the common items of private sets without disclosing any other information beyond the result; thus, it has gained significant importance in various distributed computation scenarios. As the number of participants increases, the performance of multi-party PSI protocols is significantly influenced, primarily by the number of interaction rounds needed. In this study, we propose two novel multi-party PSI protocols: the first one is 1MPSI, and the second is 2MPSI. The 1MPSI appears as a wheel structure where the parties need only one round of interactive communication online. 1MPSI is based on the Ring version of Oblivious Linear-function Evaluation (OLE). Benefiting from the wheel structure, 1MPSI supports parallel computation and achieves competitive efficiency between the leader and the other participants (OLE receivers) after input-independent precomputation. The 2MPSI adopts a dual-core star structure and introduces Oblivious Key-Value Store (OKVS), which results in better performance when handling larger set sizes and more participants. Our protocols are designed with simplicity and ease of implementation in mind. Experimental evaluations demonstrate the superiority of our protocols over current open-source multi-party PSI protocols as the set size increases from 212to 220when involving 10 and 16 parties. In a test with 16 parties each inputting 220elements, 2MPSI achieves a runtime of only 65 seconds.

Environmental perception in Automated Valet Parking (AVP) has been a challenging task due to severe occlusions in parking garages. Although Collaborative Perception (CP) can be applied to broaden the field of view of connected vehicles, the limited bandwidth of vehicular communications restricts its application. In this work, we propose a BEV feature-based CP network architecture for infrastructure-assisted AVP systems. The model takes the roadside camera and LiDAR as optional inputs and adaptively fuses them with onboard sensors in a unified BEV representation. Autoencoder and downsampling are applied for channel-wise and spatial-wise dimension reduction, while sparsification and quantization further compress the feature map with little loss in data precision. Combining these techniques, the size of a BEV feature map is effectively compressed to fit in the feasible data rate of the NR-V2X network. With the synthetic AVP dataset, we observe that CP can effectively increase perception performance, especially for pedestrians. Moreover, the advantage of infrastructure-assisted CP is demonstrated in two typical safety-critical scenarios in the AVP setting, increasing the maximum safe cruising speed by up to 3m/s in both scenarios.