Secure and Scalable Circuit-based Protocol for Multi-Party Private Set Intersection

We propose a novel protocol for computing a circuit which implements the multi-party private set intersection functionality (PSI). Circuit-based approach has advantages over using custom protocols to achieve this task, since many applications of PSI do not require the computation of the intersection itself, but rather specific functional computations over the items in the intersection. Our protocol represents the pioneering circuit-based multi-party PSI protocol, which builds upon and optimizes the two-party SCS \cite{huang2012private} protocol. By using secure computation between two parties, our protocol sidesteps the complexities associated with multi-party interactions and demonstrates good scalability. In order to mitigate the high overhead associated with circuit-based constructions, we have further enhanced our protocol by utilizing simple hashing scheme and permutation-based hash functions. These tricks have enabled us to minimize circuit size by employing bucketing techniques while simultaneously attaining noteworthy reductions in both computation and communication expenses

Numerical simulation on directional solidification of Al-Ni-Co alloy based on FEM

The ratio, of the temperature gradient at the solidification front to the solidification rate of solid-liquid interface, plays a large part in columnar grain growth. The transient temperature fields of directional solidification of Al-Ni-Co alloy were studied by employing a finite element method. The temperature gradient at the solidification front and the solidification rate were analyzed for molten steels pouring at different temperatures. The results show that with different initial pouring temperatures, the individual ratio of the temperature gradient at solidification front to the solidification rate soars up in the initial stage of solidification, then varies within 2,000-6,000 ℃·s·cm-2, and finally goes down rapidly and even tend to be closed to each other when the solidification thickness reaches 5-6 cm. The simulation result is consistent with the practical production which can provide an available reference for process optimization of directional solidified Al-Ni-Co alloy

Adaptive Local Steps Federated Learning with Differential Privacy Driven by Convergence Analysis

Federated Learning (FL) is a distributed machine learning technique that allows model training among multiple devices or organizations without sharing data. However, while FL ensures that the raw data is not directly accessible to external adversaries, adversaries can still obtain some statistical information about the data through differential attacks. Differential Privacy (DP) has been proposed, which adds noise to the model or gradients to prevent adversaries from inferring private information from the transmitted parameters. We reconsider the framework of differential privacy federated learning in resource-constrained scenarios (privacy budget and communication resources). We analyze the convergence of federated learning with differential privacy (DPFL) on resource-constrained scenarios and propose an Adaptive Local Steps Differential Privacy Federated Learning (ALS-DPFL) algorithm. We experiment our algorithm on the FashionMNIST and Cifar-10 datasets and achieve quite good performance relative to previous work