Fully homomorphic encryption (FHE) based database outsourcing is drawing growing research interests. At its current state, there exist two primary obstacles against FHE-based encrypted databases (EDBs): i) low data precision, and ii) high computational latency. To tackle the precision-performance dilemma, we introduce ArcEDB, a novel FHE-based SQL evaluation infrastructure that simultaneously achieves high data precision and fast query evaluation. Based on a set of new plaintext encoding schemes, we are able to execute arbitrary-precision ciphertext-to-ciphertext homomorphic comparison orders of magnitude faster than existing methods. Meanwhile, we propose efficient conversion algorithms between the encoding schemes to support highly composite SQL statements, including advanced filter-aggregation and multi-column synchronized sorting. We perform comprehensive experiments to study the performance characteristics of ArcEDB. In particular, we show that ArcEDB can be up to 57× faster in homomorphic filtering and up to 20× faster over end-to-end SQL queries when compared to the state-of-the-art FHE-based EDB solutions. Using ArcEDB, a SQL query over a 10K-row time-series EDB with 64-bit timestamps only runs for under one minute.

This work proposes a novel design of a Transformer architecture model for ionospheric total electron content (TEC) forecasting called Ionformer. This model is conceptually derived from the Informer model and incorporates patching and learnable position encoding to enhance the focus on local semantic information in the embedding of ionospheric TEC data and enables it to effectively capture complex patterns in it. In our experiments, using ionospheric data from the Crustal Dynamics Data Information System (CDDIS) of NASA and seven data analysis centers of IGS, we processed it into a $15\times 18$ grid of global ionospheric TEC data and forecast a high-solar activity year (2014) and a low-solar activity year (2017). We also compare the performance of Ionformer and other models in different experimental environments, including ionospheric forecasts in different years, locations, and periods of the solar cycle. The results and discussion show that the predictions of our model substantially outperform the other models and are well adapted to both ionospheric magnetic storm periods and quiet periods. An additional experiment shows that the model also outperforms other models for long-term TEC forecasts. In the above experiments, compared with the widely used LSTM-based models, our proposed model significantly improves the prediction performance of ionospheric TEC and can accurately capture the complex patterns of electron density distribution in the ionosphere, ensuring the reliable propagation of Global Navigation Satellite System (GNSS) signals and providing more reliable support for the stable operation of global navigation and communication systems.

A multitude of toxic online behaviors, ranging from network attacks to anonymous traffic and spam, have severely disrupted the smooth operation of networks. Due to the inherent sender-receiver nature of network behaviors, graph-based frameworks are commonly used for detecting anomalous behaviors. However, in real-world scenarios, the boundary between normal and anomalous behaviors tends to be ambiguous. The local heterophily of graphs interferes with the detection, and existing methods based on nodes or edges introduce unwanted noise into representation results, thereby impacting the effectiveness of detection. To address these issues, we propose PhoGAD, a graph-based anomaly detection framework. PhoGAD leverages persistent homology optimization to clarify behavioral boundaries. Building upon this, the weights of adjacent edges are designed to mitigate the effects of local heterophily. Subsequently, to tackle the noise problem, we conduct a formal analysis and propose a disentangled representation-based explicit embedding method, ultimately achieving anomaly behavior detection. Experiments on intrusion, traffic, and spam datasets verify that PhoGAD has surpassed the performance of state-of-the-art (SOTA) frameworks in detection efficacy. Notably, PhoGAD demonstrates robust detection even with diminished anomaly proportions, highlighting its applicability to real-world scenarios. The analysis of persistent homology demonstrates its effectiveness in capturing the topological structure formed by normal edge features. Additionally, ablation experiments validate the effectiveness of the innovative mechanisms integrated within PhoGAD.