High-Confidence Computing最新文献

To protect the privacy of power data, we usually encrypt data before outsourcing it to the cloud servers. However, it is challenging to search over the encrypted data. In addition, we need to ensure that only authorized users can retrieve the power data. The attribute-based searchable encryption is an advanced technology to solve these problems. However, many existing schemes do not support large universe, expressive access policies, and hidden access policies. In this paper, we propose an attribute-based keyword search encryption scheme for power data protection. Firstly, our proposed scheme can support encrypted data retrieval and achieve fine-grained access control. Only authorized users whose attributes satisfy the access policies can search and decrypt the encrypted data. Secondly, to satisfy the requirement in the power grid environment, the proposed scheme can support large attribute universe and hidden access policies. The access policy in this scheme does not leak private information about users. Thirdly, the security analysis and performance analysis indicate that our scheme is efficient and practical. Furthermore, the comparisons with other schemes demonstrate the advantages of our proposed scheme.

Intellectual Property (IP) includes ideas, innovations, methodologies, works of authorship (viz., literary and artistic works), emblems, brands, images, etc. This property is intangible since it is pertinent to the human intellect. Therefore, IP entities are indisputably vulnerable to infringements and modifications without the owner’s consent. IP protection regulations have been deployed and are still in practice, including patents, copyrights, contracts, trademarks, trade secrets, etc., to address these challenges. Unfortunately, these protections are insufficient to keep IP entities from being changed or stolen without permission. As for this, some IPs require hardware IP protection mechanisms, and others require software IP protection techniques. To secure these IPs, researchers have explored the domain of Intellectual Property Protection (IPP) using different approaches. In this paper, we discuss the existing IP rights and concurrent breakthroughs in the field of IPP research; provide discussions on hardware IP and software IP attacks and defense techniques; summarize different applications of IP protection; and lastly, identify the challenges and future research prospects in hardware and software IP security.

The Internet of Things is a modern technology that is directed at easing human life by automating most of the things used in daily life. The never-ending dependency on the network for communication is attracting adversaries to exploit the vulnerabilities of IoT. Therefore, this technology is facing some serious issues and challenges concerning security and privacy. These issues and challenges are the real motivation behind considering this study. Hence, this survey includes a discussion about security and privacy challenges as well as available solutions for IoT based wireless sensor networks. This systematic literature review (SLR) focuses particularly on a popular and applicable security approach known as Trust Management System (TMS). Firstly, all aspects of trust management, including trust indicators, trust properties, trust evaluation, trust building, trust models and the importance of those models for security and privacy, trust prediction methodologies, and ultimately trust-based attacks, are covered in this literature. Secondly, trust management schemes are classified into four groups based on the methodology used for trust-based security solutions in the IoT: cryptography-based, computational and probabilistic-based, information theory-based, and others. Then, an understanding of the problems and difficulties with current methodologies is given, along with suggestions for further research. Finally, the SLR is concluded by formulating the desirable characteristics of a trust management system in the IoT and proposing a trust model suitable for IoT networks.

Resistive Random-Access Memory (ReRAM) based Processing-in-Memory (PIM) frameworks are proposed to accelerate the working process of DNN models by eliminating the data movement between the computing and memory units. To further mitigate the space and energy consumption, DNN model weight sparsity and weight pattern repetition are exploited to optimize these ReRAM-based accelerators. However, most of these works only focus on one aspect of this software/hardware co-design framework and optimize them individually, which makes the design far from optimal. In this paper, we propose PRAP-PIM, which jointly exploits the weight sparsity and weight pattern repetition by using a weight pattern reusing aware pruning method. By relaxing the weight pattern reusing precondition, we propose a similarity-based weight pattern reusing method that can achieve a higher weight pattern reusing ratio. Experimental results show that PRAP-PIM achieves 1.64× performance improvement and 1.51× energy efficiency improvement in popular deep learning benchmarks, compared with the state-of-the-art ReRAM-based DNN accelerators.

Internet of things (IoT) has become more popular due to the development and potential of smart technology aspects. Security concerns against IoT infrastructure, applications, and devices have grown along with the need for IoT technologies. Enhanced system security protocols are difficult due to the diverse capabilities of IoT devices and the dynamic, ever-changing environment, and simply applying basic security requirements is dangerous. Therefore, this proposed work designs a malware detection and prevention approach for secure data transmission among IoT gadgets. The malware detection approach is designed with the aid of a deep learning approach. The initial process is identifying attack nodes from normal nodes through a trust value using contextual features. After discovering attack nodes, these are considered for predicting different kinds of attacks present in the network, while some preprocessing and feature extraction strategies are applied for effective classification. The Deep LSTM classifier is applied for this malware detection approach. Once completed malware detection, prevention is performed with the help of the Improved Elliptic Curve Cryptography (IECC) algorithm. A hybrid MA-BW optimization is adopted for selecting the optimal key during transmission. Python 3.8 software is used to test the performance of the proposed approach, and several existing techniques are considered to evaluate its performance. The proposed approach obtained 95% of accuracy, 5% of error value and 92% of precision. In addition, the improved ECC algorithm is also compared with some existing algorithm which takes 6.02 s of execution time. Compared to the other methods, the proposed approach provides better security to IoT gadgets during data transmission.

With the rapid development of high-speed mobile network technology and high-precision positioning technology, the trajectory information of mobile users has received extensive attention from academia and industry in the field of Location-based Social Networks. Researchers can mine users’ trajectories in Location-based Social Networks to obtain sensitive information, such as friendship groups, activity patterns, and consumption habits. Therefore, mobile users’ privacy and security issues have received growing attention in Location-based Social networks. It is crucial to strike a balance between privacy protection and data availability. This paper proposes a differential privacy trajectory protection method based on latent interest community detection (DPTP-LICD), ensuring strict privacy protection standards and user data availability. Firstly, based on the historical trajectory information of users, spatiotemporal constraint information is extracted to construct a potential community strength model for mobile users. Secondly, the latent interest community obtained from the analysis is used to identify preferred hot spots on the user’s trajectory, and their priorities are assigned based on a popularity model. A reasonable privacy budget is allocated to prevent excessive noise from being added and rendering the protected trajectory data unusable. Finally, to prevent privacy leakage, we add Laplace and exponential noise in generating preferred hot spots and recommending user interest points. Security and effectiveness analysis shows that our mechanism provides effective points of interest recommendations and protects users’ privacy from disclosure.

There is an exponential increase in the number of smart devices, generating helpful information and posing a serious challenge while processing this huge data. The processing is either done at fog level or cloud level depending on the size and nature of the task. Offloading data to fog or cloud adds latency, which is less in fog and more in the cloud. The methods of processing data and tasks at fog level or cloud are mostly machine learning based. In this paper, we will discuss all three levels in terms of architecture, starting from the internet of things to fog and fog to cloud. Specifically, we will describe machine learning-based offloading from the internet of things to fog and fog to cloud. Finally, we will come up with current research directions, issues, and challenges in the IoT–fog–cloud environment.

A smart wheelchair provides mobility assistance to persons with motor disabilities by processing sensory inputs from the person. This involves accurately collecting inputs from the user during various movement activities and using them to determine their intended motion. These smart wheelchairs work by collecting brain signals in the form of electroencephalography (EEG) signals and by processing them into a quantized format to provide movement assistance to people. Such systems can be referred to as brain–computer interface (BCI) systems that work with EEG signals. Acquiring data from human beings in the form of brain signals through EEG, along with processing of those signals and ensuring the correctness of actions instigated by those brain signals involve a huge amount of data. In this work, we carried out an experiment by taking 100 human subjects and recording their brain signals using a NeuroMax device. Typical wheelchairs are constrained by design as the motion of those is limited either by manual operation or controlled by haptic sensors and actuators. The main objective in this work was to design a wheelchair with better usability and control using machine learning-based knowledge, which is typically a data-driven approach. However, the proposed approach was designed to take inputs from human gestures and brain sensory activities to provide better usability to the wheelchair. The attention meditation cost–benefit analysis (AMCBA) proposed in this paper aims to reduce the risk of inappropriate results and improve performance by considering various cost–benefit parameters. The said classifier aims to improve the quality of emotion recognition by filtering features from EEG signals using methods of feature selection. The operation of the proposed method is described in two steps: in the first step, we assign weights to different channels for the extraction of spatial and temporal information from human behavior. The second step presents the cost–benefit model to improve the accuracy to help in decision-making. Moreover, we tried to assess the performance of the wheelchair for various assumptions and technical specifications. Finally, this study achieves improved performance in the most difficult circumstances to provide a better experience to persons with immobility.

Smart grid enhances the intelligence of the traditional power grid, which allows sharing varied data such as consumer, production, or energy with service consumers. Due to the untrustworthy networks, there exist potential security threats (e.g., unauthorized access and modification, malicious data theft) hindering the development of smart grid. While several access control schemes have been proposed for smart grid to achieve sensitive data protection and fine-grained identity management, most of them cannot satisfy the requirements of decentralizing smart grid environment and suffer from key escrow problems. In addition, some existing solutions cannot achieve dynamic user management for lacking the privilege revocation mechanism. In this paper, we propose a decentralizing access control system with user revocation to relieve the above problems. We design a new multiple-authority attribute-based encryption (MABE) scheme to keep data confidentiality and adapt decentralizing smart grid applications. We also compare our proposal with the similar solution from both security and performance. The comparing results show that our access control system can achieve a trade-off among confidentiality, authentication, distribution and efficiency in smart grid.