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.

An attribute-based access control scheme using blockchain technology for IoT data protection

With the wide application of the Internet of Things(IoT),storing large amounts of IoT data and protecting data privacy has become a meaningful issue.In general,the access control mechanism is used to prevent illegal users from accessing private data.However,traditional data access control schemes face some non-ignorable problems,such as only supporting coarse-grained access control,the risk of centralization,and high trust issues.In this paper,an attribute-based data access control scheme using blockchain technology is proposed.To address these problems,attribute-based encryption(ABE)has become a promising solution for encrypted data access control.Firstly,we utilize blockchain technology to construct a decentralized access control scheme,which can grant data access with transparency and traceability.Furthermore,our scheme also guarantees the privacy of policies and attributes on the blockchain network.Secondly,we optimize an ABE scheme,which makes the size of system parameters smaller and improves the efficiency of algorithms.These optimizations enable our proposed scheme supports large attribute universe requirements in IoT environments.Thirdly,to prohibit attribute impersonation and attribute replay attacks,we design a challenge-response mechanism to verify the ownership of attributes.Finally,we evaluate the security and performance of the scheme.And comparisons with other related schemes show the advantages of our proposed scheme.Compared to existing schemes,our scheme has more comprehensive advantages,such as supporting a large universe,full security,expressive policy,and policy hiding.

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.

PERSONAL DATA PROTECTION： RETHINKING THE REASONS, NATURE, AND LEGAL FRAMEWORK

This paper rethinks the reasons for and the nature and means of personal data protection. The reasons for personal data protection are that it could promote the fairness and effectiveness of information flow, help individuals develop their independent personality, and equip them to deal with risks. With respect to the nature of personal data, this paper argues that such data should not be perceived from a purely individualistic point of view. Rather, there should be a contextualized understanding of the data, which considers the appropriate information flow of personal data within a particular context. Regarding the legal framework of personal data protection, this paper suggests that consumer protection law and public law are better equipped to protect personal data than tort, contract, or property law.

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.

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.

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.

The Influence of Abnormal Data on Relay Protection

The parameters of abnormal data are defined and their influence on the original data and Fourier Algorithm is studied. A formula is proposed to quantify how the abnormal data influences the amplitude calculated by Fourier Algorithm. Two simulation models are established in Matlab to study the influence of abnormal data on relay protection. The simulation results show that the abnormal data can make distance protection extend the fault’s influence and make over current protection start by error.

Privacy Cost Analysis and Privacy Protection Based on Big Data

A comprehensive analysis of the impact privacy incidents on its market value is given.A broad set of instances of the exposure of personal information from a summary of some security mechanisms and the corresponding results are presented. The cumulative effect increases in magnitude over day following the breach announcement, but then decreases. Besides, a new privacy protection property, that is, p-sensitive k-anonymity is presented in this paper to protect against identity disclosure. We illustrated the inclusion of the two necessary conditions in the algorithm for computing a p-k-minimal generalization. Algorithms such as k-anonymity and l-diversity remain all sensitive attributes intact and apply generalization and suppression to the quasi-identifiers. This will keep the data "truthful" and provide good utility for data-mining applications, while achieving less perfect privacy. We aim to get the problem based on the prior analysis, and study the issue of privacy protection from the perspective of the model-benefit.

The Information Protection in Automatic Reconstruction of Not Continuous Geophysical Data Series

We show a quantitative technique characterized by low numerical mediation for the reconstruction of temporal sequences of geophysical data of length L interrupted for a time ΔT where . The aim is to protect the information acquired before and after the interruption by means of a numerical protocol with the lowest possible calculation weight. The signal reconstruction process is based on the synthesis of the low frequency signal extracted for subsampling (subsampling &#8711Dirac = ΔT in phase with ΔT) with the high frequency signal recorded before the crash. The SYRec (SYnthetic REConstruction) method for simplicity and speed of calculation and for spectral response stability is particularly effective in the studies of high speed transient phenomena that develop in very perturbed fields. This operative condition is found a mental when almost immediate informational responses are required to the observation system. In this example we are dealing with geomagnetic data coming from an uw counter intrusion magnetic system. The system produces (on time) information about the transit of local magnetic singularities (magnetic perturbations with low spatial extension), originated by quasi-point form and kinematic sources (divers), in harbors magnetic underwater fields. The performances of stability of the SYRec system make it usable also in long and medium period of observation (activity of geomagnetic observatories).

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.

Application and Research of Meteorological Data in Lightning Protection Technical Service of Oil Depot

In this paper,lightning protection technical service of oil depot is taken as research object. It is proposed using various meteorological data to carry out multiple lightning protection technical services( risk assessment,potential forecast,monitoring and early warning of lightning disasters) based on conventional detection of lightning protection system,to improve technical content of lightning protection service and benefits of disaster prevention and mitigation. It mainly analyzes the application of lightning monitoring data in pre-assessment of lightning disaster,to guide site selection of new oil depot; guidance of lightning disaster status assessment on regular inspection of lightning protection system,to determine lightning protection detection time and update and maintenance time of lightning protection system; potential prediction and early warning of lightning,to guide daily operation of oil depot.

A Compound Relay Protection Operation Criterion Based on Kirchhoff’s Current Law and Abnormal Data Detecting Algorithm

A compound relay protection operation criterion is proposed based on Kirchhoff’s Current Law and abnormal data detecting algorithm. The abnormal data detecting algorithm are proposed after deep research on the abnormal data properties. The current transformer status monitoring system and current phase angle detecting system is introduced. A simulation model containing different power sources and loads is established in Matlab. The simulation results show that this compound criterion can work quickly and reliably in all conditions.

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.

Delineation of Well Head Protection Areas for the Public Wells in the Ferizaj Region (Kosovo) with Limited Data Availability

The Swiss Agency for Development and Cooperation (SDC) has funded the Rural Water and Sanitation Support Programme (RWSSP) that has increased the access to public water supply throughout Europe’s youngest state—Kosovo—in the past ten years. The Programme, implemented by Dorsch International Consultants GmbH and Community Development Initiatives has, among other activities, implemented groundwater protection methods. Nevertheless, groundwater protection remains a challenge in Kosovo. The water law describes that water source protection is similar to German rules, yet modelling-based planning of water source protection zones remains challenging. In the present study, the development of the hydrogeological and the mathematical groundwater model for the technical delineation of the well head protection area for the Ferizaj well fields under limited data availability is described in detail. The study shows that even when not all data are available, it is possible and necessary to use mathematical groundwater models to delineate well head protection areas.

Ending Privacy’s Gremlin: Stopping the Data-Broker Loophole to the Fourth Amendment’s Search Warrant Requirement

Advances in technology require upgrades in the law. One such area involves data brokers, which have thus far gone unregulated. Data brokers use artificial intelligence to aggregate information into data profiles about individual Americans derived from consumer use of the internet and connected devices. Data profiles are then sold for profit. Government investigators use a legal loophole to purchase this data instead of obtaining a search warrant, which the Fourth Amendment would otherwise require. Consumers have lacked a reasonable means to fight or correct the information data brokers collect. Americans may not even be aware of the risks of data aggregation, which upends the test of reasonable expectations used in a search warrant analysis. Data aggregation should be controlled and regulated, which is the direction some privacy laws take. Legislatures must step forward to safeguard against shadowy data-profiling practices, whether abroad or at home. In the meantime, courts can modify their search warrant analysis by including data privacy principles.

Oracle Data Guard与Oracle Streams技术对比

Oracle Data Guard和Oracle Streams是提高数据库可用性,构建灾难备份系统以及实现数据库分布的理想的技术解决方案。探讨Oracle Data Guard和Oracle Streams技术的实现原理以及技术特点。

多普勒雷达DATA文件安全模型的设计

从计算机DATA文件安全的内涵出发,针对目前DATA文件面临的安全威胁,分析了影响DATA文件的安全因素,提出了DATA文件安全体系层次结构。重点论述DATA文件层次的安全控制所采取的技术手段和应急措施,在此基础上设计了一种新型DATA文件安全防护模型,使之能够控制DATA文件多层安全,保证DATA文件的安全运行。