Differentially Private Graph Neural Network with Importance-Grained Noise Adaption

Graph Neural Networks (GNNs) with differential privacy have been proposed to preserve graph privacy when nodes represent personal and sensitive information. However, the existing methods ignore that nodes with different importance may yield diverse privacy demands, which may lead to over-protect some nodes and decrease model utility. In this paper, we study the problem of importance-grained privacy, where nodes contain personal data that need to be kept private but are critical for training a GNN. We propose NAP-GNN, a node-importance-grained privacy-preserving GNN algorithm with privacy guarantees based on adaptive differential privacy to safeguard node information. First, we propose a Topology-based Node Importance Estimation (TNIE) method to infer unknown node importance with neighborhood and centrality awareness. Second, an adaptive private aggregation method is proposed to perturb neighborhood aggregation from node-importance-grain. Third, we propose to privately train a graph learning algorithm on perturbed aggregations in adaptive residual connection mode over multi-layers convolution for node-wise tasks. Theoretically analysis shows that NAP-GNN satisfies privacy guarantees. Empirical experiments over real-world graph datasets show that NAP-GNN achieves a better trade-off between privacy and accuracy.

Hierarchical Codebook Design and Analytical Beamforming Solution for IRS Assisted Communication

In intelligent reflecting surface (IRS) assisted communication, beam search is usually time-consuming as the multiple-input multiple-output (MIMO) of IRS is usually very large. Hierarchical codebooks is a widely accepted method for reducing the complexity of searching time. The performance of this method strongly depends on the design scheme of beamforming of different beamwidths. In this paper, a non-constant phase difference (NCPD) beamforming algorithm is proposed. To implement the NCPD algorithm, we first model the phase shift of IRS as a continuous function, and then determine the parameters of the continuous function through the analysis of its array factor. Then, we propose a hierarchical codebook and two beam training schemes, namely the joint searching (JS) scheme and direction-wise searching (DWS) scheme by using the NCPD algorithm which can flexibly change the width, direction and shape of the beam formed by the IRS array. Simulation results show that the NCPD algorithm is more accurate with smaller side lobes, and also more stable on IRS of different sizes compared to other wide beam algorithms. The misalignment rate of the beam formed by the NCPD method is significantly reduced. The time complexity of the NCPD algorithm is constant, thus making it more suitable for solving the beamforming design problem with practically large IRS.

When UAVs Meet ISAC: Real-Time Trajectory Design for Secure Communications

The real-time unmanned aerial vehicle (UAV) trajectory design of secure integrated sensing and communication (ISAC) is optimized. In particular, the UAV serves both as a downlink transmitter and a radar receiver. The legitimate user (Bob) roams on ground through a series of unknown locations, while the eavesdropper moves following a fixed known trajectory. To maximize the real-time secrecy rate, we propose an extended Kalman filtering (EKF)-based method for tracking and predicting Bob's location at the UAV based on the delay measurements extracted from the sensing echoes. We then formulate a non-convex real-time trajectory design problem and develop an efficient iterative algorithm for finding a near optimal solution. Our numerical results demonstrate that the proposed algorithm is capable of accurately tracking Bob and strikes a compelling legitimate vs. leakage rate trade-off.

Privacy Inference-Empowered Stealthy Backdoor Attack on Federated Learning under Non-IID Scenarios

Federated learning (FL) naturally faces the problem of data heterogeneity in real-world scenarios, but this is often overlooked by studies on FL security and privacy. On the one hand, the effectiveness of backdoor attacks on FL may drop significantly under non-IID scenarios. On the other hand, malicious clients may steal private data through privacy inference attacks. Therefore, it is necessary to have a comprehensive perspective of data heterogeneity, backdoor, and privacy inference. In this paper, we propose a novel privacy inference-empowered stealthy backdoor attack (PI-SBA) scheme for FL under non-IID scenarios. Firstly, a diverse data reconstruction mechanism based on generative adversarial networks (GANs) is proposed to produce a supplementary dataset, which can improve the attacker's local data distribution and support more sophisticated strategies for backdoor attacks. Based on this, we design a source-specified backdoor learning (SSBL) strategy as a demonstration, allowing the adversary to arbitrarily specify which classes are susceptible to the backdoor trigger. Since the PI-SBA has an independent poisoned data synthesis process, it can be integrated into existing backdoor attacks to improve their effectiveness and stealthiness in non-IID scenarios. Extensive experiments based on MNIST, CIFAR10 and Youtube Aligned Face datasets demonstrate that the proposed PI-SBA scheme is effective in non-IID FL and stealthy against state-of-the-art defense methods.

Optimizing the Collaboration Structure in Cross-Silo Federated Learning

In federated learning (FL), multiple clients collaborate to train machine learning models together while keeping their data decentralized. Through utilizing more training data, FL suffers from the potential negative transfer problem: the global FL model may even perform worse than the models trained with local data only. In this paper, we propose FedCollab, a novel FL framework that alleviates negative transfer by clustering clients into non-overlapping coalitions based on their distribution distances and data quantities. As a result, each client only collaborates with the clients having similar data distributions, and tends to collaborate with more clients when it has less data. We evaluate our framework with a variety of datasets, models, and types of non-IIDness. Our results demonstrate that FedCollab effectively mitigates negative transfer across a wide range of FL algorithms and consistently outperforms other clustered FL algorithms.

FakeSwarm: Improving Fake News Detection with Swarming Characteristics

The proliferation of fake news poses a serious threat to society, as it can misinform and manipulate the public, erode trust in institutions, and undermine democratic processes. To address this issue, we present FakeSwarm, a fake news identification system that leverages the swarming characteristics of fake news. To extract the swarm behavior, we propose a novel concept of fake news swarming characteristics and design three types of swarm features, including principal component analysis, metric representation, and position encoding. We evaluate our system on a public dataset and demonstrate the effectiveness of incorporating swarm features in fake news identification, achieving an f1-score and accuracy of over 97% by combining all three types of swarm features. Furthermore, we design an online learning pipeline based on the hypothesis of the temporal distribution pattern of fake news emergence, validated on a topic with early emerging fake news and a shortage of text samples, showing that swarm features can significantly improve recall rates in such cases. Our work provides a new perspective and approach to fake news detection and highlights the importance of considering swarming characteristics in detecting fake news.

MedLens: Improve mortality prediction via medical signs selecting and regression interpolation

Monitoring the health status of patients and predicting mortality in advance is vital for providing patients with timely care and treatment. Massive medical signs in electronic health records (EHR) are fitted into advanced machine learning models to make predictions. However, the data-quality problem of original clinical signs is less discussed in the literature. Based on an in-depth measurement of the missing rate and correlation score across various medical signs and a large amount of patient hospital admission records, we discovered the comprehensive missing rate is extremely high, and a large number of useless signs could hurt the performance of prediction models. Then we concluded that only improving data-quality could improve the baseline accuracy of different prediction algorithms. We designed MEDLENS, with an automatic vital medical signs selection approach via statistics and a flexible interpolation approach for high missing rate time series. After augmenting the data-quality of original medical signs, MEDLENS applies ensemble classifiers to boost the accuracy and reduce the computation overhead at the same time. It achieves a very high accuracy performance of 0.96% AUC-ROC and 0.81% AUC-PR, which exceeds the previous benchmark.

FineEHR: Refine Clinical Note Representations to Improve Mortality Prediction

Monitoring the health status of patients in the Intensive Care Unit (ICU) is a critical aspect of providing superior care and treatment. The availability of large-scale electronic health records (EHR) provides machine learning models with an abundance of clinical text and vital sign data, enabling them to make highly accurate predictions. Despite the emergence of advanced Natural Language Processing (NLP) algorithms for clinical note analysis, the complex textual structure and noise present in raw clinical data have posed significant challenges. Coarse embedding approaches without domain-specific refinement have limited the accuracy of these algorithms. To address this issue, we propose FINEEHR, a system that utilizes two representation learning techniques, namely metric learning and fine-tuning, to refine clinical note embeddings, while leveraging the intrinsic correlations among different health statuses and note categories. We evaluate the performance of FINEEHR using two metrics, namely Area Under the Curve (AUC) and AUC-PR, on a real-world MIMIC III dataset. Our experimental results demonstrate that both refinement approaches improve prediction accuracy, and their combination yields the best results. Moreover, our proposed method outperforms prior works, with an AUC improvement of over 10%, achieving an average AUC of 96.04% and an average AUC-PR of 96.48% across various classifiers.

BotTriNet: A Unified and Efficient Embedding for Social Bots Detection via Metric Learning

A persistently popular topic in online social networks is the rapid and accurate discovery of bot accounts to prevent their invasion and harassment of genuine users. We propose a unified embedding framework called BotTriNet, which utilizes textual content posted by accounts for bot detection based on the assumption that contexts naturally reveal account personalities and habits. Content is abundant and valuable if the system efficiently extracts bot-related information using embedding techniques. Beyond the general embedding framework that generates word, sentence, and account embeddings, we design a triplet network to tune the raw embeddings (produced by traditional natural language processing techniques) for better classification performance. We evaluate detection accuracy and f1score on a real-world dataset CRESCI2017, comprising three bot account categories and five bot sample sets. Our system achieves the highest average accuracy of 98.34% and f1score of 97.99% on two content-intensive bot sets, outperforming previous work and becoming state-of-the-art. It also makes a breakthrough on four content-less bot sets, with an average accuracy improvement of 11.52% and an average f1score increase of 16.70%.