Deep Reinforcement Learning for Privacy-Preserving Task Offloading in Integrated Satellite-Terrestrial Networks

Satellite communication networks have attracted widespread attention for seamless network coverage and collaborative computing. In satellite-terrestrial networks, ground users can offload computing tasks to visible satellites that with strong computational capabilities. Existing solutions on satellite-assisted task computing generally focused on system performance optimization such as task completion time and energy consumption. However, due to the high-speed mobility pattern and unreliable communication channels, existing methods still suffer from serious privacy leakages. In this paper, we present an integrated satellite-terrestrial network to enable satellite-assisted task offloading under dynamic mobility nature. We also propose a privacy-preserving task offloading scheme to bridge the gap between offloading performance and privacy leakage. In particular, we balance two offloading privacy, called the usage pattern privacy and the location privacy, with different offloading targets (e.g., completion time, energy consumption, and communication reliability). Finally, we formulate it into a joint optimization problem, and introduce a deep reinforcement learning-based privacy-preserving algorithm for an optimal offloading policy. Experimental results show that our proposed algorithm outperforms other benchmark algorithms in terms of completion time, energy consumption, privacy-preserving level, and communication reliability. We hope this work could provide improved solutions for privacy-persevering task offloading in satellite-assisted edge computing

BAGEL: Backdoor Attacks against Federated Contrastive Learning

Federated Contrastive Learning (FCL) is an emerging privacy-preserving paradigm in distributed learning for unlabeled data. In FCL, distributed parties collaboratively learn a global encoder with unlabeled data, and the global encoder could be widely used as a feature extractor to build models for many downstream tasks. However, FCL is also vulnerable to many security threats (e.g., backdoor attacks) due to its distributed nature, which are seldom investigated in existing solutions. In this paper, we study the backdoor attack against FCL as a pioneer research, to illustrate how backdoor attacks on distributed local clients act on downstream tasks. Specifically, in our system, malicious clients can successfully inject a backdoor into the global encoder by uploading poisoned local updates, thus downstream models built with this global encoder will also inherit the backdoor. We also investigate how to inject backdoors into multiple downstream models, in terms of two different backdoor attacks, namely the \textit{centralized attack} and the \textit{decentralized attack}. Experiment results show that both the centralized and the decentralized attacks can inject backdoors into downstream models effectively with high attack success rates. Finally, we evaluate two defense methods against our proposed backdoor attacks in FCL, which indicates that the decentralized backdoor attack is more stealthy and harder to defend

Genomic Insights Into the Admixture History of Mongolic- and Tungusic-Speaking Populations From Southwestern East Asia

As a major part of the modern Trans-Eurasian or Altaic language family, most of the Mongolic and Tungusic languages were mainly spoken in northern China, Mongolia, and southern Siberia, but some were also found in southern China. Previous genetic surveys only focused on the dissection of genetic structure of northern Altaic-speaking populations; however, the ancestral origin and genomic diversification of Mongolic and Tungusic–speaking populations from southwestern East Asia remain poorly understood because of the paucity of high-density sampling and genome-wide data. Here, we generated genome-wide data at nearly 700,000 single-nucleotide polymorphisms (SNPs) in 26 Mongolians and 55 Manchus collected from Guizhou province in southwestern China. We applied principal component analysis (PCA), ADMIXTURE, f statistics, qpWave/qpAdm analysis, qpGraph, TreeMix, Fst, and ALDER to infer the fine-scale population genetic structure and admixture history. We found significant genetic differentiation between northern and southern Mongolic and Tungusic speakers, as one specific genetic cline of Manchu and Mongolian was identified in Guizhou province. Further results from ADMIXTURE and f statistics showed that the studied Guizhou Mongolians and Manchus had a strong genetic affinity with southern East Asians, especially for inland southern East Asians. The qpAdm-based estimates of ancestry admixture proportion demonstrated that Guizhou Mongolians and Manchus people could be modeled as the admixtures of one northern ancestry related to northern Tungusic/Mongolic speakers or Yellow River farmers and one southern ancestry associated with Austronesian, Tai-Kadai, and Austroasiatic speakers. The qpGraph-based phylogeny and neighbor-joining tree further confirmed that Guizhou Manchus and Mongolians derived approximately half of the ancestry from their northern ancestors and the other half from southern Indigenous East Asians. The estimated admixture time ranged from 600 to 1,000 years ago, which further confirmed the admixture events were mediated via the Mongolians Empire expansion during the formation of the Yuan dynasty

Fast and Accurate SNN Model Strengthening for Industrial Applications

In spiking neural networks (SNN), there are emerging security threats, such as adversarial samples and poisoned data samples, which reduce the global model performance. Therefore, it is an important issue to eliminate the impact of malicious data samples on the whole model. In SNNs, a naive solution is to delete all malicious data samples and retrain the entire dataset. In the era of large models, this is impractical due to the huge computational complexity. To address this problem, we present a novel SNN model strengthening method to support fast and accurate removal of malicious data from a trained model. Specifically, we use untrained data that has the same distribution as the training data. We can infer that the untrained data has no effect on the initial model, and the malicious data should have no effect on the final refined model. Thus, we can use the model output of the untrained data with respect to the initial model to guide the final refined model. In this way, we present a stochastic gradient descent method to iteratively determine the final model. We perform a comprehensive performance evaluation on two industrial steel surface datasets. Experimental results show that our model strengthening method can provide accurate malicious data elimination, with speeds 11.7× to 27.2× faster speeds than the baseline method

Interference-resistant heading measurement for location-based mobile services

Accurate heading information is crucial for many mobile services such as navigation, autonomous vehicles, and robot applications. The standard way to obtain heading information is by using a tri-axis magnetometer, which can measure the signal intensity of the magnetic field in three orthogonal directions. However, a magnetometer is known to be highly susceptible to environmental interferences that may easily generate error up to thousands of nano-tesla, causing unacceptable errors (e.g., 40 degrees) in the direction output and rendering the mobile service useless. We present a novel design for interference resistant heading measurement using multiple magnetometers. In this design, multiple magnetometers are placed around a circle, imitating the manual rotation of a single magnetometer. This arrangement allows us to automatically calibrate the devices against external interference, using the classic ellipse fitting method. We have implemented a system consisting of a STM32F103RC microcontroller and six AKM8975 magnetic sensors. Evaluation in realistic environments shows that our design can adaptively update the calibration parameters, reducing the original heading error from 150 degrees to below 2.5 degrees, and the heading error remains stable in environments with different patterns of magnetic perturbation

Cyclic Behavior of Multiple Hardening Precast Concrete Shear Walls

Precast Concrete (PC) shear walls are becoming popular in building structures. With “wet” connection techniques, PC shear walls often behave like conventional cast-in-place walls, where hardening occurs after yielding. In this study, two PC shear walls assembled by the “dry” connection technique, and one cast-in-place shear wall, were tested by means of quasi-static cyclic loading. The main purpose of the experiment was to systematically investigate the cyclic response of PC shear walls with varying types of vertical connection in the form of a friction-bearing device. The results showed that vertical bearing in devices, which mainly stems from the longitudinal elongation of PC wall panels, could enlarge the axial force of end column so that it provided an additional resistance moment. The PC shear wall with weak connection achieved ductile failure and second ascending branches on load-displacement relationship, i.e., secondary hardening, and the wall with strong vertical connection performed great moment capacity as well as tertiary hardening. Compared to cast-in-place walls, the peak load and cumulative hysteretic energy of PC shear walls increased by about 60% and 100%, respectively. A conceptual analysis of the multiple hardening phenomenon is presented based on experimental results