Accuracy and individual fairness are both crucial for trustworthy machine learning, but these two aspects are often incompatible with each other so that enhancing one aspect may sacrifice the other inevitably with side effects of true bias or false fairness. We propose in this paper a new fairness criterion, accurate fairness, to align individual fairness with accuracy. Informally, it requires the treatments of an individual and the individual's similar counterparts to conform to a uniform target, i.e., the ground truth of the individual. We prove that accurate fairness also implies typical group fairness criteria over a union of similar sub-populations. We then present a Siamese fairness in-processing approach to minimize the accuracy and fairness losses of a machine learning model under the accurate fairness constraints. To the best of our knowledge, this is the first time that a Siamese approach is adapted for bias mitigation. We also propose fairness confusion matrix-based metrics, fair-precision, fair-recall, and fair-F1 score, to quantify a trade-off between accuracy and individual fairness. Comparative case studies with popular fairness datasets show that our Siamese fairness approach can achieve on average 1.02%-8.78% higher individual fairness (in terms of fairness through awareness) and 8.38%-13.69% higher accuracy, as well as 10.09%-20.57% higher true fair rate, and 5.43%-10.01% higher fair-F1 score, than the state-of-the-art bias mitigation techniques. This demonstrates that our Siamese fairness approach can indeed improve individual fairness without trading accuracy. Finally, the accurate fairness criterion and Siamese fairness approach are applied to mitigate the possible service discrimination with a real Ctrip dataset, by on average fairly serving 112.33% more customers (specifically, 81.29% more customers in an accurately fair way) than baseline models.

Maneuvering-target tracking has always been an important and challenge work because the unknown and changeable motion-models can easily lead to the failure of model-driven target tracking. Recently, many neural network methods are proposed to improve the tracking accuracy by constructing direct mapping relationships from noisy observations to target states. However, limited by the coverage of training data, those data-driven methods suffer other problems, such as weak generalization abilities and unstable tracking effects. In this paper, a digital twin system for maneuvering-target tracking is built, and all kinds of simulated data are created with different motion-models. Based on those data, the features of noisy observations and their relationship to target states are found by two specially designed neural networks: one eliminates the observation noises and the other one predicts the target states according to the noise-limited observations. Combining the above two networks, the state prediction method is proposed to intelligently predict targets by understanding the information of motion-model hidden in noisy observations. Simulation results show that, in comparison with the state-of-the-art model-driven and data-driven methods, the proposed method can correctly and timely predict the motion-models, increase the tracking generalization ability and reduce the tracking root-mean-squared-error by over 50% in most of maneuvering-target tracking scenes.

Recent studies have shown that ad-hoc image transformations are effective for defending certain adversarial attacks. It is desirable to determine which transformations are more effective than others for adversarial defenses before these transformations are being deployed in practice. We propose in this paper the notion of Critical Transformation Robustness (CTR), which can indicate potentially the detection performance of an input transformation through the difference between its CTR on clean inputs and that on adversarial ones. Then, based on this new notion, we further present a general training framework that can deliver an effective and efficient adversarial detector, which features in a customized combination of specific input transformations for defending the given, possibly mixed, adversarial attacks. We evaluate our training framework on 3 typical image datasets with 17 types of popular input transformations for detecting a mixture of 22 types of adversarial attacks. Experimental results show that the CTR differences between the clean and adversarial inputs can essentially guide the selection of an effective combination of input transformations with their nearly-optimal parameter values. Furthermore, compared with the state-of-the-art input transformation-based adversarial detection methods, the detectors generated by our training framework exhibit on average 73.4% - 87.5% higher performance on the mixed adversarial attacks.

The emergence of infectious disease COVID-19 has challenged and changed the world in an unprecedented manner. The integration of wireless networks with edge computing (namely wireless edge networks) brings opportunities to address this crisis. In this paper, we aim to investigate the prediction of the infectious probability and propose precautionary measures against COVID-19 with the assistance of wireless edge networks. Due to the availability of the recorded detention time and the density of individuals within a wireless edge network, we propose a stochastic geometry-based method to analyze the infectious probability of individuals. The proposed method can well keep the privacy of individuals in the system since it does not require to know the location or trajectory of each individual. Moreover, we also consider three types of mobility models and the static model of individuals. Numerical results show that analytical results well match with simulation results, thereby validating the accuracy of the proposed model. Moreover, numerical results also offer many insightful implications. Thereafter, we also offer a number of countermeasures against the spread of COVID-19 based on wireless edge networks. This study lays the foundation toward predicting the infectious risk in realistic environment and points out directions in mitigating the spread of infectious diseases with the aid of wireless edge networks.

To protect confidential communications from eavesdropping attacks in wireless networks, we propose a novel anti-eavesdropping scheme named AE-Shelter. In our proposed scheme, we place a number of friendly jammers at a circular boundary to protect legitimate communications. The jammers sending artificial noise can mitigate the eavesdropping capability of wiretapping the confidential information. We also establish a theoretical model to evaluate the performance of AE-Shelter. Our results show that our proposed AE-Shelter scheme can significantly reduce the eavesdropping risk without significantly degrading the network performance.

This paper proposes a novel anti-eavesdropping scheme by introducing artificial noise caused by friendly jammers deployed in wireless networks. In particular, we propose an analytical model to quantify the eavesdropping risk of wireless network with friendly jammers. Our approach considers both large-scale path loss and Rayleigh fading. Our numerical results show that the eavesdropping risk of wireless networks can be significantly reduced with the aid of firendly jammers.

In this paper, we propose a novel anti- eavesdropping scheme by introducing friendly jammers to a wireless network of things (WNoT). In particular, we establish a theoretical framework to evaluate the eavesdropping risk of WNoT with friendly jammers and the eavesdropping risk of WNoT without jammers. Our theoretical model takes into account various channel conditions such as the path loss and Rayleigh fading as well as the placement schemes of jammers. Our extensive numerical results show that using jammers in WNoT can effectively reduce the eavesdropping risk. Besides, our results also show that the eavesdropping risk heavily depends on both the channel conditions and the placements of jammers.

This paper concerns with the local connectivity (i.e., the probability of node isolation) of wireless networks with directional antennas. We propose an analytical framework to study the local connectivity with the consideration of directional antenna models and various channel conditions. With the framework, we construct a novel directional antenna model called Iris. We show that Iris can better approximate realistic directional antennas and can be easily used to analyze the local connectivity compared with existing directional antenna models. Extensive simulations show that the theoretical results are in good agreement with the simulation results verifying the accuracy and the effectiveness of our analytical framework.