Privacy-preserving Security Inference Towards Cloud-Edge Collaborative Using Differential Privacy

Cloud-edge collaborative inference approach splits deep neural networks (DNNs) into two parts that run collaboratively on resource-constrained edge devices and cloud servers, aiming at minimizing inference latency and protecting data privacy. However, even if the raw input data from edge devices is not directly exposed to the cloud, state-of-the-art attacks targeting collaborative inference are still able to reconstruct the raw private data from the intermediate outputs of the exposed local models, introducing serious privacy risks. In this paper, a secure privacy inference framework for cloud-edge collaboration is proposed, termed CIS, which supports adaptively partitioning the network according to the dynamically changing network bandwidth and fully releases the computational power of edge devices. To mitigate the influence introduced by private perturbation, CIS provides a way to achieve differential privacy protection by adding refined noise to the intermediate layer feature maps offloaded to the cloud. Meanwhile, with a given total privacy budget, the budget is reasonably allocated by the size of the feature graph rank generated by different convolution filters, which makes the inference in the cloud robust to the perturbed data, thus effectively trade-off the conflicting problem between privacy and availability. Finally, we construct a real cloud-edge collaborative inference computing scenario to verify the effectiveness of inference latency and model partitioning on resource-constrained edge devices. Furthermore, the state-of-the-art cloud-edge collaborative reconstruction attack is used to evaluate the practical availability of the end-to-end privacy protection mechanism provided by CIS

The Cell Cycle Checkpoint Gene, RAD17 rs1045051, Is Associated with Prostate Cancer Risk

Human RAD17, as an agonist of checkpoint signaling, plays an essential role in mediating DNA damage. This hospital-based case-control study aimed to explore the association between RAD17 rs1045051, a missense sin-gle nucleotide polymorphism (SNP), and prostate cancer risk. Subjects were 358 prostate cancer patients and 314 cancer-free urology patients undergoing treatment at the Zhujiang Hospital of Southern Medical University in China. RAD17 gene polymorphism rs1045051 was evaluated by the SNaPshot method. Compared with the RAD17 gene polymorphism rs1045051 AA genotype, there was a higher risk of prostate cancer for the CC gen-otype (adjusted odds ratio [AOR] = 1.731, 95% confidence interval [95%CI] = 1.031−2.908, p = 0.038). Compared with the A allele, the C allele was significantly associated with the disease status (AOR = 1.302, 95%CI = 1.037−1.634, p = 0.023). All these findings indicate that in the SNP rs1045051, both the CC genotype and C allele may have a substantial influence on the prostate cancer risk

Repair and Reconstruction of a Resected Tumor Defect Using a Composite of Tissue Flap–Nanotherapeutic–Silk Fibroin and Chitosan Scaffold

A multifaceted strategy using a composite of anti-cancer nanotherapeutic and natural biomaterials silk fibroin (SF) and chitosan (CS) blend scaffolds was investigated for the treatment of a tissue defect post-tumor resection by providing local release of the therapeutic and filling of the defect site with the regenerative bioscaffolds. The scaffold-emodin nanoparticle composites were fabricated and characterized for drug entrapment and release, mechanical strength, and efficacy against GILM2 breast cancer cells in vitro and in vivo in a rat tumor model. Emodin nanoparticles were embedded in SF and SFCS scaffolds and the amount of emodin entrapment was a function of the scaffold composition and emodin loading concentration. In vitro, there was a burst release of emodin from all scaffolds during the first 2 days though it was detected even after 24 days. Increase in emodin concentration in the scaffolds decreased the overall elastic modulus and ultimate tensile strength of the scaffolds. After 6 weeks of in vivo implantation, the cell density (p < 0.05) and percent degradation (p < 0.01) within the remodeled no emodin SFCS scaffold was significantly higher than the emodin loaded SFCS scaffolds, although there was no significant difference in the amount of collagen deposition in the regenerated SFCS scaffold. The presence and release of emodin from the SFCS scaffolds inhibited the integration of SFCS into the adjacent tumor due to the formation of an interfacial barrier of connective tissue that was lacking in emodin-free SFCS scaffolds. While no significant difference in tumor size was observed between the in vivo tested groups, tumors treated with emodin loaded SFCS scaffolds had decreased presence and size and similar regeneration of new tissue as compared to no emodin SFCS scaffolds

A Sentiment-based Hybrid Model for Stock Return Forecasting

Real-world financial time series often contain both linear and nonlinear patterns. However, traditional time series analysis models, such as ARIMA, hold the assumption that a linear correlation exists among time series values while leaving nonlinear relation into error terms. Based on financial theories, we argue that investor sentiment is the main contributor to nonlinear pattern of stock time series. Furthermore, we propose a sentiment-based hybrid model (SLNM) to better capture nonlinear information in stock time series. According to the forecasting experimental results, SLNM exhibits the sensitivity to sentiment environments, which in turn supports the argument that investor sentiment is the main source of nonlinear pattern in stock time series. For those stocks that are in top 10 of CAR Ranking List ─ these stocks are more likely pursed by emotional investors and thus in optimistic sentiment environment, SLNM improves forecasting performance dramatically: Increase Direction Accuracy by 40% and reduce RMSE by 19.3%. While, for those that are in bottom 10 of CAR Ranking List─ these stocks defer more emotional investors from further participating in stock trading and thus in pessimistic sentiment environment, SLNM has a fair improvement on performance: Hold the similar Direction Accuracy and reduce RMSE only by 2.5%. All these indicate that investor sentiment play a key role in stock return forecasting. Our work sheds light on the research of sentiment-based prediction models

Enhancing cloud-based IoT security through trustworthy cloud service: An integration of security and reputation approach

The Internet of Things (IoT) provides a new paradigm for the development of heterogeneous and distributed systems, and it has increasingly become a ubiquitous computing service platform. However, due to the lack of sufficient computing and storage resources dedicated to the processing and storage of huge volumes of the IoT data, it tends to adopt a cloud-based architecture to address the issues of resource constraints. Hence, a series of challenging security and trust concerns have arisen in the cloud-based IoT context. To this end, a novel trust assessment framework for the security and reputation of cloud services is proposed. This framework enables the trust evaluation of cloud services in order to ensure the security of the cloud-based IoT context via integrating security- and reputation-based trust assessment methods. The security-based trust assessment method employs the cloud-specific security metrics to evaluate the security of a cloud service. Furthermore, the feedback ratings on the quality of cloud service are exploited in the reputation-based trust assessment method in order to evaluate the reputation of a cloud service. The experiments conducted using a synthesized dataset of security metrics and a real-world web service dataset show that our proposed trust assessment framework can efficiently and effectively assess the trustworthiness of a cloud service while outperforming other trust assessment methods.The Internet of Things (IoT) provides a new paradigm for the development of heterogeneous and distributed systems, and it has increasingly become a ubiquitous computing service platform. However, due to the lack of sufficient computing and storage resources dedicated to the processing and storage of huge volumes of the IoT data, it tends to adopt a cloud-based architecture to address the issues of resource constraints. Hence, a series of challenging security and trust concerns have arisen in the cloud-based IoT context. To this end, a novel trust assessment framework for the security and reputation of cloud services is proposed. This framework enables the trust evaluation of cloud services in order to ensure the security of the cloud-based IoT context via integrating security- and reputation-based trust assessment methods. The security-based trust assessment method employs the cloud-specific security metrics to evaluate the security of a cloud service. Furthermore, the feedback ratings on the quality of cloud service are exploited in the reputation-based trust assessment method in order to evaluate the reputation of a cloud service. The experiments conducted using a synthesized dataset of security metrics and a real-world web service dataset show that our proposed trust assessment framework can efficiently and effectively assess the trustworthiness of a cloud service while outperforming other trust assessment methods

Analysis and research on the fault-tolerant performance of the high-frequency isolated energy conversion link for an integrated distribution transformer system

In order to enhance the stability of the integrated distribution transformer system (IDTS), a fault-tolerant topology of the high-frequency (HF) isolated energy conversion link which features a multi-winding HF transformer and two multi-arm converters are proposed in this paper. The operation process of the HF-isolated energy conversion link with an open-circuit fault in the power electronic switching devices is analysed in detail, and the fault characteristics are researched. Through modifying the operation modes of the proposed topology, the damaged part of the circuit can be replaced with the redundant transformer windings and bridge arms, maintaining the continuous operation of the IDTS. The theoretical analyses are verified by the simulated operation waveforms

Looking for Gold in the Sands: Stock Prediction Using Financial News and Social Media

Both traditional finance and behavioral finance theory have reached a consensus that the news media de facto influence stock prices to some extent. There is also evidence that investors are not only subject to the sentiment of related news articles but also the public opinions. The challenge lies on how to quantify such sentimental information to predict the movement of stock market. To measure the sentiments of articles and capture the public mood from postings, we construct and maintain a sentiment dictionary. We utilize both the official information from news articles and user postings in discussion boards to predict firm-specific stock price, and differentiate various types of news articles in the predictive model. Our experiments on CSI 100 stocks during a six week period show a predictive performance in closeness to the actual future stock price is 0.03503 in terms of mean squared error, the same direction of price movement as the future price is 67.6%. Among all seven news topic categories, restructuring news of enterprises has the best predicting performance with direction accuracy of 68.18%

SCCAF: A Secure and Compliant Continuous Assessment Framework in Cloud-Based IoT Context

The Internet of Things (IoT) offers a wide variety of benefits to our daily lives in many ways, ranging from smart wearable devices to industrial systems. However, it also brings well-known security and compliance concerns, especially in the physical layer. In addition, due to numerous IoT architectures which have been developed and deployed based on the cloud, the security and compliance of IoT depend on the cloud thoroughly. In this paper, a secure and compliant continuous assessment framework (SCCAF) is proposed to evaluate the security and compliance levels of cloud services in life-cycle. The SCCAF facilitates cloud service to customers to select an optimal cloud service provider (CSP) which satisfies their desired security requirements. Moreover, it also enables cloud service customers to evaluate the compliance of the selected CSP in the process of using cloud services. To evaluate the performance and availability of SCCAF, we carry out a series of experiments with case study and real-world scenario datasets. Experimental results show that SCCAF can assess the security and compliance of CSPs efficiently and effectively

Chaotic Motion Planning for Mobile Robots: Progress, Challenges, and Opportunities

Chaotic path planners are a subset of path planning algorithms that use chaotic dynamical systems to generate trajectories throughout an environment. These path planners are imperative in surveillance tasks in the presence of adversarial agents which require the paths to be unpredictable while at the same time guaranteeing complete coverage of the environments. In the online coverage of unknown terrain, the chaotic path planning algorithms can work without the need of the environment map and the designer has additional control over the generated paths relative to other heuristic coverage path planners such as random-walk algorithms. Although chaotic path planners have been studied over the past two decades, there has not been an updated survey on the advances. This paper presents an up-to-date review by providing: an introduction of commonly used chaotic systems and methods for their manipulation; an overview of obstacle avoidance methods used by chaotic path planners; and a discussion on other applications, challenges, and research gaps