The statistical distribution of user geographic location data is widely used in various mobile applications. Although geo-indistinguishability (GI) has emerged as an effective privacy-preserving framework for processing location data, due to the lack of robust perturbation probability calculation and post-processing for eliminating statistical errors caused by random perturbation on user-side data, GI exhibits low accuracy when directly applied to two-dimensional continuous location data distribution estimation. To overcome this, we propose a novel and efficient location data distribution estimation mechanism by improving GI, termed gamma-based circle and uniform grids (GBC-UG). The GBC-UG mechanism consists of two key algorithms: i) the gamma-based circle (GBC) algorithm, which perturbs users' location data and ensures the calculability of perturbation probabilities on the server side, and ii) the uniform grids (UG) algorithm, which post-processes the perturbed data to accurately estimate the original distribution. We provide a theoretical analyses of the upper and lower bounds of the statistical error in distribution estimation and identify optimal parameter values to minimize this error. Experimental results on multiple real-world datasets demonstrate that the proposed GBC-UG mechanism can significantly improve the accuracy of distribution estimation, as well as the prediction accuracy of both the top-k and popularity ranking while guaranteeing user privacy, outperforming existing GI-based approaches.

In this paper, we focus on denoising seismic signals effectively to obtain high-quality data, which is crucially important for oil-gas reservoir prediction and seismic interpretation tasks. The rapid progress of deep learning has brought new development opportunities to seismic oil and gas exploration technologies. However, current deep learning-based seismic denoising models have limited learning ability due to insufficient extraction features in strong noise backgrounds. For this reason, we develop a new multi-scale generative adversarial networks (GAN) with transform prediction for seismic signal denoising. First, we develop a deep multi-scale diversion fusion (MSDF) network in a GAN generator considering the advantages of combining GAN and convolutional neural networks. MSDF network primarily contains several MSDF blocks that mine abundant long-short path features in multiple receptive fields to restore seismic signals in a rough to detailed manner. Additionally, current deep learning-based seismic denoising models only exploit features in the spatial domain, not considering high-frequency characteristics, which results in insufficient high-frequency details when reconstructing seismic data. So, we propose to predict high-frequency components during learning by employing the superior non-subsampled contourlet transform (NSCT), which further preserves the better global topological structure and local texture characteristics of MSDF in GAN generator than the spatial domain, promoting the discrimination ability in GAN discriminator. The qualitative and quantitative results on our constructed synthetic dataset and actual seismic data demonstrate that the proposed method surpasses other deep learning-based approaches in realizing higher signal-to-noise ratio, as well as mining more effective high-frequency signals.

A 24-bit sensor Read-out Integrated Circuit (ROIC) with a ±5V input range is presented in this paper. The system utilizes a two-opamp programmable gain amplifier (PGA) and a third-order incremental switched-capacitor sigma-delta (SC Σ∆) analog-to-digital converter (ADC) to achieve a front-end gain ranging from 1 to 128. The PGA comprises a gain-boosting amplification stage and a class-AB output stage, effectively eliminating offset and 1/f noise through chopping. The ADC adapts a configurable zero optimization loop, allowing enhanced noise transfer function (NTF) at different output data rates (ODRs). Double-sampling technique is used to further improve the Signalto-Noise Ratio (SNR). This system is realized in 0.18µm standard CMOS process, providing three power modes and supporting up to four input signal channels. It exhibits a temperature coefficient of less than 2 ppm/°C over the operating range of -40 °C to 105 °C, while achieving a maximum Effective Resolution (ER) of 23.8 bits at a ODR of 10 samples per second (SPS). The calculated Figure of Merit (FoM) for the proposed ROIC reaches 173.7 dB.

This paper presents a high area and power efficiency incremental hybrid Continuous-Time/Discrete-Time Sigma-Delta Modulator (CT/DT SDM) based Current-to-Digital Converter (CDC). The system utilizes a resistor-free continuous-time integrator as the first stage and a Switched-Capacitor (SC) integrator as the second stage to achieve second order noise shaping, leading to significant reduction in the overall area and power consumption when current signal is served as sensor output. The integration of a recycling folded-cascode (RFC) op-amp further contributes to power reduction, while the enhanced charge injection cancellation switch further improve the harmonic and noise characteristics of the system. The proposed CDC achieves 102.5dB Signal-to-Noise and Distortion Ratio (SNDR) with total current consumption of only 54.8µA and an area of 0.08 mm2. The calculated Figure of Merit (FoM) for the system is 172.5dB, indicating an outstanding trade-off between system resolution and power consumption.

We propose a robust seismic noise attenuation method by an alternative denoising procedure using variational mode decomposition (VMD) combined with the Schrodinger equation thresholding, referred as VMD-Schrodinger interval-thresholding method. In the proposed method, VMD is used as a subband-like filter to get a series of uncorrelated intrinsic mode functions (IMFs) from low frequency to high frequency. For IMFs having strong correlations with the original signal, the Schrodinger equation thresholding-based denoising is used to obtain the denoised IMFs. For IMFs existing weak correlations with the original signal, the noise extracted from these IMFs is used to generate several different versions of noise by altering the sample positions. These different versions of noise combined with the information extracted from these IMFs are used to reconstruct different noisy versions of the original IMFs existing weak correlations. Then the Schrodinger equation thresholding-based denoising is applied to these different noisy data. The denoised IMFs existing weak correlations are the average of these denoised noisy versions signals. Finally, the denoised seismic data is the sum of these denoised IMFs. Compared with the wavelet thresholding method, iterative EMD interval-thresholding method and quantum mechanics-based signal denoising method, the proposed method can more effectively extract weak signals from the noise-dominant components of seismic data, while protecting both the low-frequency and high-frequency components of the seismic trace, especially exhibiting good performance for complex dipping events. The synthetic and field data applications illustrate the robustness and the superiority of the proposed method.

Host-based intrusion detection systems (HIDS) have been widely acknowledged as an effective approach for detecting and mitigating malicious activities. Among various data sources utilized in HIDS, system call traces have gained significant popularity due to their inherent advantage of providing fine-grained information. Nevertheless, conventional feature extraction techniques relying on system calls tend to overlook the issue of high-dimensional sparse feature space. In this paper, we conduct a theoretical analysis to investigate the underlying causes of the sparsity problem. Subsequently, we propose an anti-sparse theory (anti-ST) as a solution to address this issue. Then, we design a multi-granularity feature extraction method (MGFE), which also meets the prerequisite mathematical conditions of the anti-ST. By applying this method, we effectively reduce the size of the feature space and minimize the number of generated features, thus mitigating sparsity. Furthermore, leveraging this approach, we propose a robust and anti-sparsity host intrusion detection framework, known as the MGFE-based Host Intrusion Detection Framework (BR-HIDF). A series of experiments were conducted to evaluate the proposed framework and compare it with the state-of-the-art method. The results demonstrate that our framework achieves impressive accuracy (97.26%), precision (97.62%), recall (96.85%), and F1 score (97.23%) in the intrusion detection task, surpassing existing frameworks. Moreover, the proposed framework significantly reduces the time overhead by 38.80%, exhibiting the highest AUC value of 0.992. Furthermore, we enhance the robustness of the detection system by integrating host-based and network-based detection, which provides greater flexibility in identifying various types of attacks.

The application of graph convolutional network in short-term traffic flow forecasting of road network has effectively improved the prediction accuracy. The key point of this method is to construct the Laplacian matrix through extracting spatial features among nodes of the road network. However, most available methods mainly rely on the spatial distance among nodes to construct Laplacian matrix, then optimized the Laplacian matrix by other methods, which limits the wide application of the model. In this paper, we propose a dynamic spatial-temporal graph attention graph convolutional network (GAGCN) method to improve the generality of the model. The Laplacian matrix in this model is constructed directly by the dependencies among the nodes hidden in the traffic data which are identified by the graph attention networks, and can be dynamic adjust over time, the information of spatial distance among nodes and human intervention are not required in the process. Experimental results of two real-world datasets show that both the generality and prediction accuracy of the proposed model had been significantly improved.

The guaranteed delivery model is widely used in online advertising. The publisher sells impressions in advance by promising to serve each advertiser an agreed-upon number of target impressions that satisfy specific attribute requirements over a fixed time period. Previous efforts usually model the service as a crowd-level or user-level supply allocation problem and focus on searching optimal allocation for online serving, assuming that forecasts of supply are available and contracts are already signed. Existing techniques are not sufficient to meet the needs of today's industry trends: 1) advertisers pursue more precise targeting, which requires not only user-level attributes but also request-level attributes; 2) users prefer more friendly ad serving, which imposes more diverse serving constraints; 3) the bottleneck of the publisher's revenue growth lies in not only the ad serving, but also the forecast accuracy and sales strategy. These issues are non-trivial to address, since the scale of the request-level model is orders of magnitude larger than that of the crowd-level or user-level models. Facing the challenges, we present a holistic design of a request-level guaranteed delivery advertising planning system with careful optimization for all three critical components including impression forecasting, selling and serving. Our system has been deployed in the Tencent online guaranteed delivery advertising system serving billion level users for nearly one year. Evaluations on large-scale real data and the performance of the deployed system both demonstrate that our design can significantly increase the request-level impression forecast accuracy and delivery rate.

We perform a large-scale study to quantify just how severe the privacy leakage problem is in Facebook. As a case study, we focus on estimating birth year, which is a fundamental human attribute and, for many people, a private one. Specifically, we attempt to estimate the birth year of over 1 million Facebook users in New York City. We examine the accuracy of estimation procedures for several classes of users: (i) highly private users, who do not make their friend lists public; (ii) users who hide their birth years but make their friend lists public. To estimate Facebook users' ages, we exploit the underlying social network structure to design an iterative algorithm, which derives age estimates based on friends' ages, friends of friends' ages, and so on. We find that for most users, including highly private users who hide their friend lists, it is possible to estimate ages with an error of only a few years. We also make a specific suggestion to Facebook which, if implemented, would greatly reduce privacy leakages in its service.

Private attributes of Online Social Network (OSN) users can be inferred from other information (which is usually from users' friends and group information). To address this, social networking sites allow users to hide their friend lists and group lists, so that general public cannot see them. However, if a user doesn't make his friend list public, but his friends have public friend list where we can find him, we can do reverse lookup to extend the friend lists of the user. Furthermore, many social networks allow non-group members to list the members of public groups (e.g., Face book). These are strong violations of OSN users' privacy, and can be considered as privacy risks caused by the asymmetric configuration of settings in OSNs. In this paper we present the privacy risks due to the lack of symmetric configurations, which exist in most of the OSNs. To make our idea more clear, we propose a inference attack and show that it can be used to infer users' private information, even users already made their friend list private. We theoretically analyze the risk of proposed privacy issues, and evaluate the risk using experiments based on real-world OSN data. We show that it is not sufficient to only disable friend list and group list to guarantee privacy, and propose methods to mitigate these privacy issues.

To protect our networks against malicious intrusions, we need to evaluate these networks security. Previous works on attack graphs have provided meaningful conclusions on security measurement. However, large attack graphs are still hard to be understood vividly, and few suggestions have been proposed to prevent inside malicious attackers from attacking networks. To address these problems, we propose a novel approach to evaluate network security based on adjacency matrixes, which are constructed from existing attack graphs. With our model, we use gray scale images to show overall security vividly, and get quantitative evaluation scores. Moreover, we create a prioritized list of potential threatening hosts, which can help network administrators to harden network step by step. Analysis on computation cost shows that the upper bound computation cost of our measurement methodology is O(N3), which could be completed in real time. We also give an example to show how to put our methods in practice.

When a host discovers that it has a software vulnerability that is susceptible to an attack, the host needs to obtain and install a patch. Because centralized distribution of patches may not scale well, peer-to-peer (P2P) approaches have recently been suggested. There is, however, a serious privacy problem with peer-assisted patch distribution: when a peer A requests a patch from another peer B, it announces to B its vulnerability, which B can exploit instead of providing the patch. Through analytical modeling and simulation, we show that a large majority of vulnerable hosts will typically become compromised with a basic design for peer- assisted patch distribution. We then study the effectiveness of two different approaches in countering this privacy problem. The first approach utilizes special-purpose peer nodes, referred to as honeypots, that discover and blacklist malicious peers listening for patch requests from susceptible hosts. In the second approach, the patches are requested through an anonymizing network, hiding the identities of susceptible hosts from malicious peers. Using analytical models and simulation, we show that, honeypots do not completely solve the privacy problem; in contrast, an anonymizing network turns out to be more suitable for security patch distribution.

Attack graphs play important roles in analyzing network security vulnerabilities, and previous works have provided meaningful conclusions on the generation and security measurement of attack graphs. However, it is still hard for us to understand attack graphs in a large network, and few suggestions have been proposed to prevent inside malicious attackers from attacking networks. To address these problems, we propose a novel approach to generate and describe attack graphs. Firstly, we construct a two-layer attack graph, where the upper layer is a hosts access graph and the lower layer is composed of some host-pair attack graphs. Compared with previous works, our attack graph has simpler structures, and reaches the best upper bound of computation cost in O(N2). Furthermore, we introduce the adjacency matrix to efficiently evaluate network security, with overall evaluation results presented by gray scale images vividly. Thirdly, by applying prospective damage and important weight factors on key hosts with crucial resources, we can create prioritized lists of potential threatening hosts and stepping stones, both of which can help network administrators to harden network security. Analysis on computation cost shows that the upper bound computation cost of our measurement methodology is O(N3), which could also be completed in real time. Finally, we give some examples to show how to put our methods in practice.