The COVID-19 and the“European Solution”of Data-based Approach to Governance:Public Security or Data Protection?

To cope with the challenges of CoViD-19,europe has adopted relevant measures of a data-based approach to governance,on which scholars have huge differences,and the related researches are conducive to further discussion on the differences.By sorting out the challenges posed by the pandemic to public security and data protection in europe,we can summarize the“european Solution”of the data-based approach to governance,including legislation,instruments,supervision,international cooperation,and continuity.The“Solution”has curbed the spread of the pandemic to a certain extent.However,due to the influence of the traditional values of the EU,the“Solution”is too idealistic in the balance between public security and data protection,which intensifies the dilemma and causes many problems,such as ambiguous legislation,inadequate effectiveness and security of instruments,an arduous endeavor in inter national cooperation,and imperfect regulations on digital green certificates.Therefore,in a major public health crisis,there is still a long way to go in exploring a balance between public security and data protection.

Multi-Source Data Privacy Protection Method Based on Homomorphic Encryption and Blockchain

Multi-Source data plays an important role in the evolution of media convergence.Its fusion processing enables the further mining of data and utilization of data value and broadens the path for the sharing and dissemination of media data.However,it also faces serious problems in terms of protecting user and data privacy.Many privacy protectionmethods have been proposed to solve the problemof privacy leakage during the process of data sharing,but they suffer fromtwo flaws:1)the lack of algorithmic frameworks for specific scenarios such as dynamic datasets in the media domain;2)the inability to solve the problem of the high computational complexity of ciphertext in multi-source data privacy protection,resulting in long encryption and decryption times.In this paper,we propose a multi-source data privacy protection method based on homomorphic encryption and blockchain technology,which solves the privacy protection problem ofmulti-source heterogeneous data in the dissemination ofmedia and reduces ciphertext processing time.We deployed the proposedmethod on theHyperledger platformfor testing and compared it with the privacy protection schemes based on k-anonymity and differential privacy.The experimental results showthat the key generation,encryption,and decryption times of the proposedmethod are lower than those in data privacy protection methods based on k-anonymity technology and differential privacy technology.This significantly reduces the processing time ofmulti-source data,which gives it potential for use in many applications.

Residential Energy Consumption Forecasting Based on Federated Reinforcement Learning with Data Privacy Protection

Most studies have conducted experiments on predicting energy consumption by integrating data formodel training.However, the process of centralizing data can cause problems of data leakage.Meanwhile,many laws and regulationson data security and privacy have been enacted, making it difficult to centralize data, which can lead to a datasilo problem. Thus, to train the model while maintaining user privacy, we adopt a federated learning framework.However, in all classical federated learning frameworks secure aggregation, the Federated Averaging (FedAvg)method is used to directly weight the model parameters on average, which may have an adverse effect on te model.Therefore, we propose the Federated Reinforcement Learning (FedRL) model, which consists of multiple userscollaboratively training the model. Each household trains a local model on local data. These local data neverleave the local area, and only the encrypted parameters are uploaded to the central server to participate in thesecure aggregation of the global model. We improve FedAvg by incorporating a Q-learning algorithm to assignweights to each locally uploaded local model. And the model has improved predictive performance. We validatethe performance of the FedRL model by testing it on a real-world dataset and compare the experimental results withother models. The performance of our proposed method in most of the evaluation metrics is improved comparedto both the centralized and distributed models.

Perception of privacy and data protection in the context of the development of artificial intelligence

Customer privacy perception and the principles of its regulatory protection determine how the tech sector is operating,striking a new balance between economic winners and losers.Nevertheless,not all countries that are leaders in the latest technologies are strongly in favor of flexible and pro-business regulations.This can be clearly seen in the field of artificial intelligence(AI).Self-regulation as a key strategic approach to AI may be seen as an essential factor of broader implementation of AI solutions.The purpose of this paper is to present approaches to AI while indicating the differences that result from the understanding of privacy,increasing customers privacy concerns and regulations related to data privacy which come together with official administrative strategies.The impact of AI implementation on relationships between customers and companies has been emphasized and analyzed in the context of regulations and customer perception of privacy.

A Private User Data Protection Mechanism in TrustZone Architecture Based on Identity Authentication

In Trust Zone architecture, the Trusted Application（TA） in the secure world does not certify the identity of Client Applications（CA） in the normal world that request data access, which represents a user data leakage risk. This paper proposes a private user data protection mechanism in Trust Zone to avoid such risks. We add corresponding modules to both the secure world and the normal world and authenticate the identity of CA to prevent illegal access to private user data. Then we analyze the system security, and perform validity and performance tests.The results show that this method can perform effective identity recognition and control of CA to protect the security of private user data. After adding authentication modules, the data operation time of system increases by about0.16 s, an acceptable price to pay for the improved security.

To Whom Do Data Belong?——Data Ownership and Protection in the Context of Web-Crawlers

Platform data has already become an important asset for web-based companies,but this sort of data frequently includes large amounts of personal information.Platform data can be seen as belonging to an individual,belonging to a platform,belonging to some combinations of the two,or can be seen as a form of Internet-based public data.Analysis of legal clauses and doctrines as well as analysis based in legitimacy and consequentialism both fail to completely delineate data ownership.One potential reason for this is that there are many types of platform data,and that each type is highly dependent on circumstances.The determination of rights in regard to platform data should be done in a way which revolves around a contextual regulatory framework,one in which the rules of reason is applied on a case-by-case basis and in which gradual changes are done in a bottom-up manner,and not one which seeks to establish a universal set of data regulations.In actual judgments,factors such as the nature of the platform and the nature of the data crawling behavior should be comprehensively considered while ensuring a balance of data circulation and data protection.

Cybersecurity and Privacy Protection in Vehicular Networks (VANETs)

As Vehicular ad hoc networks (VANETs) become more sophisticated, the importance of integrating data protection and cybersecurity is increasingly evident. This paper offers a comprehensive investigation into the challenges and solutions associated with the privacy implications within VANETs, rooted in an intricate landscape of cross-jurisdictional data protection regulations. Our examination underscores the unique nature of VANETs, which, unlike other ad-hoc networks, demand heightened security and privacy considerations due to their exposure to sensitive data such as vehicle identifiers, routes, and more. Through a rigorous exploration of pseudonymization schemes, with a notable emphasis on the Density-based Location Privacy (DLP) method, we elucidate the potential to mitigate and sometimes sidestep the heavy compliance burdens associated with data protection laws. Furthermore, this paper illuminates the cybersecurity vulnerabilities inherent to VANETs, proposing robust countermeasures, including secure data transmission protocols. In synthesizing our findings, we advocate for the proactive adoption of protective mechanisms to facilitate the broader acceptance of VANET technology while concurrently addressing regulatory and cybersecurity hurdles.

Development of data acquisition and over-current protection systems for a suppressor-grid current with a neutral-beam ion source

Neutral beam injection is one of the effective auxiliary heating methods in magnetic-confinementfusion experiments. In order to acquire the suppressor-grid current signal and avoid the grid being damaged by overheating, a data acquisition and over-current protection system based on the PXI（PCI e Xtensions for Instrumentation） platform has been developed. The system consists of a current sensor, data acquisition module and over-current protection module. In the data acquisition module,the acquired data of one shot will be transferred in isolation and saved in a data-storage server in a txt file. It can also be recalled using NBWave for future analysis. The over-current protection module contains two modes： remote and local. This gives it the function of setting a threshold voltage remotely and locally, and the forbidden time of over-current protection also can be set by a host PC in remote mode. Experimental results demonstrate that the data acquisition and overcurrent protection system has the advantages of setting forbidden time and isolation transmission.

On the Right to the Protection of Personal Data as a Constitutional Right

The right to the protection of personal data is an important human right in the era of big data and a constitutional right based on the national protection obligation and the theory of human dignity,making it of special significance for the realization of citizenship in a digital society.It can be seen from an examination of the constitutional texts of various countries in the world that the right to the protection of personal data as a constitutional right has rich normative connotations,and the key legal link to realize this right lies in the national legislature actively fulfilling its obligation to shape and specify the protection of personal data in accordance with the entrustment of the constitutional norms.Given the constitutional principles of fundamental rights protection,i.e.,realizing the constitutional status of the right to the protection of personal data as a basic right by means of institutional guarantees,the legislature should first adhere to the constitutionality principle of data protection legislation.Second,a multi-level data protection legal system centered on the right to the protection of personal data should be established.Finally,the institutional guarantee mechanism for the protection of personal data should be continuously improved through constitutional interpretation.

Dynamics analysis and cryptographic implementation of a fractional-order memristive cellular neural network model

Due to the fact that a memristor with memory properties is an ideal electronic component for implementation of the artificial neural synaptic function,a brand-new tristable locally active memristor model is first proposed in this paper.Here,a novel four-dimensional fractional-order memristive cellular neural network(FO-MCNN)model with hidden attractors is constructed to enhance the engineering feasibility of the original CNN model and its performance.Then,its hardware circuit implementation and complicated dynamic properties are investigated on multi-simulation platforms.Subsequently,it is used toward secure communication application scenarios.Taking it as the pseudo-random number generator(PRNG),a new privacy image security scheme is designed based on the adaptive sampling rate compressive sensing(ASR-CS)model.Eventually,the simulation analysis and comparative experiments manifest that the proposed data encryption scheme possesses strong immunity against various security attack models and satisfactory compression performance.

Cyber Resilience through Real-Time Threat Analysis in Information Security

This paper examines how cybersecurity is developing and how it relates to more conventional information security. Although information security and cyber security are sometimes used synonymously, this study contends that they are not the same. The concept of cyber security is explored, which goes beyond protecting information resources to include a wider variety of assets, including people [1]. Protecting information assets is the main goal of traditional information security, with consideration to the human element and how people fit into the security process. On the other hand, cyber security adds a new level of complexity, as people might unintentionally contribute to or become targets of cyberattacks. This aspect presents moral questions since it is becoming more widely accepted that society has a duty to protect weaker members of society, including children [1]. The study emphasizes how important cyber security is on a larger scale, with many countries creating plans and laws to counteract cyberattacks. Nevertheless, a lot of these sources frequently neglect to define the differences or the relationship between information security and cyber security [1]. The paper focus on differentiating between cybersecurity and information security on a larger scale. The study also highlights other areas of cybersecurity which includes defending people, social norms, and vital infrastructure from threats that arise from online in addition to information and technology protection. It contends that ethical issues and the human factor are becoming more and more important in protecting assets in the digital age, and that cyber security is a paradigm shift in this regard [1].

Competencies in Computer Security and Resolution of Technological Problems in Higher Education Students

Starting from the importance of preserving our information and protecting our devices against attacks on their vulnerabilities, this article aims to establish the level of knowledge in computer security and problem-solving of students at a university in northwest Mexico. This research has a quantitative approach where the European Framework of Digital Competencies for Citizenship was used as a reference to identify and establish the level of the competencies that were evaluated. The IKANOS Test was used as a data collection tool. The results show that students know the importance of keeping their devices safe and how valuable the information found on them is. On the other hand, the results also show a considerable percentage of students who do not have the knowledge and are at a basic level of knowledge to solve technical problems with their devices.

Few-Shot Graph Classification with Structural-Enhanced Contrastive Learning for Graph Data Copyright Protection

Open-source licenses can promote the development of machine learning by allowing others to access,modify,and redistribute the training dataset.However,not all open-source licenses may be appropriate for data sharing,as some may not provide adequate protections for sensitive or personal information such as social network data.Additionally,some data may be subject to legal or regulatory restrictions that limit its sharing,regardless of the licensing model used.Hence,obtaining large amounts of labeled data can be difficult,time-consuming,or expensive in many real-world scenarios.Few-shot graph classification,as one application of meta-learning in supervised graph learning,aims to classify unseen graph types by only using a small amount of labeled data.However,the current graph neural network methods lack full usage of graph structures on molecular graphs and social network datasets.Since structural features are known to correlate with molecular properties in chemistry,structure information tends to be ignored with sufficient property information provided.Nevertheless,the common binary classification task of chemical compounds is unsuitable in the few-shot setting requiring novel labels.Hence,this paper focuses on the graph classification tasks of a social network,whose complex topology has an uncertain relationship with its nodes'attributes.With two multi-class graph datasets with large node-attribute dimensions constructed to facilitate the research,we propose a novel learning framework that integrates both meta-learning and contrastive learning to enhance the utilization of graph topological information.Extensive experiments demonstrate the competitive performance of our framework respective to other state-of-the-art methods.

Cyber Security-Protecting Personal Data

Many organizations have datasets which contain a high volume of personal data on individuals,e.g.,health data.Even without a name or address,persons can be identified based on the details(variables)on the dataset.This is an important issue for big data holders such as public sector organizations(e.g.,Public Health Organizations)and social media companies.This paper looks at how individuals can be identified from big data using a mathematical approach and how to apply this mathematical solution to prevent accidental disclosure of a person’s details.The mathematical concept is known as the“Identity Correlation Approach”(ICA)and demonstrates how an individual can be identified without a name or address using a unique set of characteristics(variables).Secondly,having identified the individual person,it shows how a solution can be put in place to prevent accidental disclosure of the personal details.Thirdly,how to store data such that accidental leaks of the datasets do not lead to the disclosure of the personal details to unauthorized users.

An Enhanced Security System Using Blockchain Technology for Strong FMC Relationship

Blockchain technology is a shared database of logs of all consumer transactions which are registered on all machines on a network.Both transactions in the system are carried out by consensus processes and to preserve confidentiality all thefiles contained cannot be changed.Blockchain technology is the fundamental software behind digital currencies like Bitcoin,which is common in the marketplace.Cloud computing is a method of using a network of external machines to store,monitor,and process information,rather than using the local computer or a local personal computer.The software is currently facing multiple problems including lack of data protection,data instability,and reliability.This paper aims to give the highest security for multiple user environments in cloud for storing and accessing the data in blocks.The users who are legitimate are only allowed for storing and accessing the data as like a secured block chain approach.As like the Blockchain which does not require a centralized system for transactions,the proposed system is also independent on centralized network interface.The decentralized system is developed in such a way to avoid counterfeiting.The system enables the fabricator to spend less or null resources to perform the validations for its direct operated stores.This ensures the product fabricator to avoid the circulation of its duplicate products.The customer as an end-user is also ensured to have only the genuine products from the fabricator.The Fabricator(F),Merchant(M)and consumer(C)forms an interconnected triangular structure without allowing any counterfeiting agents in their secured cloud chain.The pro-posed approach provides the stability in the security system of the cloud using the chaining mechanism within different blocks at each node.It takes roughly 4.7,6.2,and 7.5 ms,respectively,to register each node in the proposed system for 5,10,and 15 nodes.The overall registration time for each scenario is 11.9,26.2,and 53.1 ms,despite the fact that each node’s registration time was greatest for 10 nodes.By looking at the data,it’s clear that the number of nodes is a func-tion of time.

Detecting Phishing Using a Multi-Layered Social Engineering Framework

As businesses develop and expand with a significant volume of data,data protection and privacy become increasingly important.Research has shown a tremendous increase in phishing activities during and after COVID-19.This research aimed to improve the existing approaches to detecting phishing activities on the internet.We designed a multi-layered phish detection algorithm to detect and prevent phishing applications on the internet using URLs.In the algorithm,we considered technical dimensions of phishing attack prevention and mitigation on the internet.In our approach,we merge,Phishtank,Blacklist,Blocklist,and Whitelist to form our framework.A web application system and browser extension were developed to implement the algorithm.The multi-layer phish detector evaluated ten thousandURLs gathered randomly from the internet(five thousand phishing and five thousand legitimate URLs).The system was estimated to detect levels of accuracy,true-positive and false-positive values.The system level accuracy was recorded to be 98.16%.Approximately 49.6%of the websites were detected as illegitimate,whilst 49.8%were seen as legitimate.

美国跨境数据流动的国际法规制路径与中国的因应

作为互联网大国,美国在规制跨境数据流动方面从一开始就确立了重数据自由流动而轻政府监管的路径,并将之贯穿于历届政府的对外贸易政策,以及世界贸易组织框架下的相关议题谈判和自由贸易协定谈判。这样的路径背后体现出美国国内法与国际法的紧密联系、更加关注消费者权利保护等特点。对其他国家所采取的限制数据流动的措施,美国常将之认定为'数据保护主义',但这样的认定带有明显的'双重标准'。对于我国而言,除了完善有关个人隐私保护的国内法,也应当通过政府间的磋商机制与美国展开双边层面的谈判,厘清数据保护主义与数据保护之间的界限。从宏观层面上看,我国也应当推动国际社会针对跨境数据流动形成多边国际法规范。

浅析基于持续数据保护技术下的智慧校园建设

智慧校园是利用信息化技术来辅助传统校园进行教育管理的一种新模式。本文从我国高校智慧校园建设中数据保护的现状出发,结合持续数据保护技术运用,融入基于多维链表IO日志记录算法、卷镜像中断后快速增量初始化方法、高可靠多粒度变尺度自适应容错技术、可视化集中管控防御技术等提示数据安全机制,对智慧校园建设提出了改进策略,具有一定推广价值。

U.S.and International Legal Perspectives Affecting Cybersecurity Corporate Governance

International and U.S.corporations must be well advised regarding specific regulations and laws that affect cybersecurity decisions because the Board of Directors must perform due diligence to avoid regulatory negligence and lawsuit liability.Depending on the standards and the regulations that do define reasonable care,the corporate director is faced with the challenge of determining how and what cybersecurity laws apply.Then,directors can institute best cybersecurity management practices.This paper provides guidance regarding the application of the law in the areas of cyber security for the international corporations interacting with the European General Data Protection Regulations(GDPR),the California Consumer Privacy Act(CCPA),and recent Federal Trade Commission(FTC)administrative agency rulings.Reading this paper is worth your time because it will inform you of the legal challenges that international and domestic corporations face in making decisions about spending capital to manage cybersecurity and at the same time perform due diligence.In other words,if there is a cybersecurity breach,this paper will provide insights into what law must be followed by the corporation enabling the best management decisions assuring adequate response,compliance,thereby avoiding unnecessary liability risk.The paper also provides reflections about whether the GDPR serves as a better legal comprehensive regulatory model rather than the recently enacted laws in the U.S.

International Cybersecurity Breach Detection Risk, Prosecution and Resolution

The U.S.and the international business community is in a crisis regarding ransomware attacks and cybersecurity policing/regulation.Based on recent events,state sponsored cyber hackers have made it their mission to hold the information security world hostage extorting money for a returning of data systems to normal functioning.This paper examines the challenges for the global business community regarding the scope of the attacks,the current state of the law in the U.S.intended to provide current information to the international business community,illustrate the challenges for regulators and the courts,illustrate the recent progress of the criminal investigatory aspects of apprehending cybercriminals,report on the non-delegable duties of decision makers in the international community doing business in the U.S.,and demonstrate the potential solutions to this evolving international crisis.It is worth your time to read this paper for the following reasons:1.Understanding the risks and the scope of the problem moving forward with doing business in the U.S.;2.communicating recent developments fostering cooperation and contributing to mediating cybersecurity events for international corporations;and 3.providing information on the current state of the law by the U.S.Supreme Court pointing out the relevant and the consequential outcomes of the legal system leading to potential smart solutions for planning business decisions.