A Practical Study of Big Data Technology in Computer Network Information Security Processing

In recent years,China has witnessed continuous development and progress in its scientific and technological landscape,with widespread utilization of computer networks.Concurrently,issues related to computer network information security,such as information leakage and virus invasions,have become increasingly prominent.Consequently,there is a pressing need for the implementation of effective network security measures.This paper aims to provide a comprehensive summary and analysis of the challenges associated with computer network information security processing.It delves into the core concepts and characteristics of big data technology,exploring its potential as a solution.The study further scrutinizes the application strategy of big data technology in addressing the aforementioned security issues within computer networks.The insights presented in this paper are intended to serve as a valuable reference for individuals involved in the relevant fields,offering guidance on effective approaches to enhance computer network information security through the application of big data technology.

Information Privacy, Data Surveillance and Security——How Australian Privacy Law Fully Plays Its Role in the Age of Big Data

Privacy and the protection of privacy is a common topic studied by many scholars. From the very beginning of human culture, people have personal privacy, which is not willing for them to be unveiled by others. With the development of information technology, especially the internet, knowledge and information are dealt by internet users in conscious or unconscious way, and personal information has been rapidly and quickly distributed and disseminated all over the world. Personal data can be collected by hackers or interlinks from the website, internet not only provides people an era with internet links, but also an age with information collections, a big data age. With the background of big data, this essay tries to put forward the correlative relationship between the protection of information privacy and the privacy law in Australia. It ??rst has an overview of the concepts of information privacy and data surveillance under the background of big data, then highlights the importance of data security in the age of big data;with a literature review on the development of Australian privacy acts, it further claims that privacy acts or regulations by the federal or states provided strong support for the protection of personal data. Then relationship between the protection information privacy and the need of judicial guarantee is further studied for thorough methods or regimes in data protection. With these points studied, this essay aims to highlight the importance of data protection and information privacy. On the other hand, it aims to provide awareness for readers the vital role privacy laws can play in the protection of people’s personal information and emphases the importance of a continuous evolution for privacy law system in the age of big data.

Challenges and Solutions of Information Security Issues in the Age of Big Data

Big data has been taken as a Chinese national strategy in order to satisfy the developments of the social and economic requirements and the development of new information technology. The prosperity of big data brings not only convenience to people's daily life and more opportunities to enterprises, but more challenges with information security as well. This paper has a research on new types and features of information security issues in the age of big data, and puts forward the solutions for the above issues: build up the big data security management platform, set up the establishment of information security system and implement relevant laws and regulations.

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].

Interpretation of Information Security and Data Privacy Protection According to the Data Use During the Epidemic

COVID-19 has swept the whole our country and the world in the beginning of 2020.31 provinces and municipalities across the country have launched the first-level response to major public health emergencies since January 24,and China has carried out intensive epidemic control.It is critical for effectively responding to COVID-19 to collect,collate and analyze people’s personal data.What’s more,obtaining identity information,travel records and health information of confirmed cases,suspected cases and close contacts has become a crucial step in epidemic investigation.All regions have made full use of big data to carry out personnel screening,travel records analysis and other related work in epidemic prevention and control,effectively improving the efficiency of epidemic prevention and control.However,data leakage,personnel privacy data exposure,and personal attack frequently occurred in the process of personnel travel records analysis and epidemic prevention and control.It even happened in the WeChat group to forward a person’s name,phone number,address,ID number and other sensitive information.It brought discrimination,telephone and SMS harassment to the parties,which caused great harm to individuals.Based on these,lack of information security and data security awareness and other issues were exposed.Therefore,while big data has been widely concerned and applied,attention should be paid to protecting personal privacy.It is urgent to pay more attention to data privacy and information security in order to effectively protect the legitimate rights of the people.Therefore,measures can be taken to achieve this goal,such as improving the relevant legal system,strengthening technical means to enhance the supervision and management of information security and data protection.

Evaluating Security of Big Data Through Fuzzy Based Decision-Making Technique

In recent years,it has been observed that the disclosure of information increases the risk of terrorism.Without restricting the accessibility of information,providing security is difficult.So,there is a demand for time tofill the gap between security and accessibility of information.In fact,security tools should be usable for improving the security as well as the accessibility of information.Though security and accessibility are not directly influenced,some of their factors are indirectly influenced by each other.Attributes play an important role in bridging the gap between security and accessibility.In this paper,we identify the key attributes of accessibility and security that impact directly and indirectly on each other,such as confidentiality,integrity,availability,and severity.The significance of every attribute on the basis of obtained weight is important for its effect on security during the big data security life cycle process.To calculate the proposed work,researchers utilised the Fuzzy Analytic Hierarchy Process(Fuzzy AHP).Thefindings show that the Fuzzy AHP is a very accurate mechanism for determining the best security solution in a real-time healthcare context.The study also looks at the rapidly evolving security technologies in healthcare that could help improve healthcare services and the future prospects in this area.

Classification of Big Data Security Based on Ontology Web Language

A vast amount of data (known as big data) may now be collected and stored from a variety of data sources, including event logs, the internet, smartphones, databases, sensors, cloud computing, and Internet of Things (IoT) devices. The term “big data security” refers to all the safeguards and instruments used to protect both the data and analytics processes against intrusions, theft, and other hostile actions that could endanger or adversely influence them. Beyond being a high-value and desirable target, protecting Big Data has particular difficulties. Big Data security does not fundamentally differ from conventional data security. Big Data security issues are caused by extraneous distinctions rather than fundamental ones. This study meticulously outlines the numerous security difficulties Large Data analytics now faces and encourages additional joint research for reducing both big data security challenges utilizing Ontology Web Language (OWL). Although we focus on the Security Challenges of Big Data in this essay, we will also briefly cover the broader Challenges of Big Data. The proposed classification of Big Data security based on ontology web language resulting from the protégé software has 32 classes and 45 subclasses.

Fifty-Six Big Data V’s Characteristics and Proposed Strategies to Overcome Security and Privacy Challenges (BD2)

The amount of data that is traveling across the internet today, including very large and complex set of raw facts that are not only large, but also, complex, noisy, heterogeneous, and longitudinal data as well. Companies, institutions, healthcare system, mobile application capturing devices and sensors, traffic management, banking, retail, education etc., use piles of data which are further used for creating reports in order to ensure continuity regarding the services that they have to offer. Recently, Big data is one of the most important topics in IT industry. Managing Big data needs new techniques because traditional security and privacy mechanisms are inadequate and unable to manage complex distributed computing for different types of data. New types of data have different and new challenges also. A lot of researches treat with big data challenges starting from Doug Laney’s landmark paper, during the previous two decades;the big challenge is how to operate a huge volume of data that has to be securely delivered through the internet and reach its destination intact. The present paper highlights important concepts of Fifty-six Big Data V’s characteristics. This paper also highlights the security and privacy Challenges that Big Data faces and solving this problem by proposed technological solutions that help us avoiding these challenging problems.

Exploring the Characteristics of Data Breaches: A Descriptive Analytic Study

Data breaches have massive consequences for companies, affecting them financially and undermining their reputation, which poses significant challenges to online security and the long-term viability of businesses. This study analyzes trends in data breaches in the United States, examining the frequency, causes, and magnitude of breaches across various industries. We document that data breaches are increasing, with hacking emerging as the leading cause. Our descriptive analyses explore factors influencing breaches, including security vulnerabilities, human error, and malicious attacks. The findings provide policymakers and businesses with actionable insights to bolster data security through proactive audits, patching, encryption, and response planning. By better understanding breach patterns and risk factors, organizations can take targeted steps to enhance protections and mitigate the potential damage of future incidents.

Data Mining with Privacy Protection Using Precise Elliptical Curve Cryptography

Protecting the privacy of data in the multi-cloud is a crucial task.Data mining is a technique that protects the privacy of individual data while mining those data.The most significant task entails obtaining data from numerous remote databases.Mining algorithms can obtain sensitive information once the data is in the data warehouse.Many traditional algorithms/techniques promise to provide safe data transfer,storing,and retrieving over the cloud platform.These strategies are primarily concerned with protecting the privacy of user data.This study aims to present data mining with privacy protection(DMPP)using precise elliptic curve cryptography(PECC),which builds upon that algebraic elliptic curve infinitefields.This approach enables safe data exchange by utilizing a reliable data consolidation approach entirely reliant on rewritable data concealing techniques.Also,it outperforms data mining in terms of solid privacy procedures while maintaining the quality of the data.Average approximation error,computational cost,anonymizing time,and data loss are considered performance measures.The suggested approach is practical and applicable in real-world situations according to the experimentalfindings.

Construction of Smart City Spatio-Temporal Information Cloud Platform in Weifang,China

On the basis of the digital Weifang geospatial framework,Smart Weifang spatio-temporal information cloud platform(WFCP)integrated legal person information,population,place name and address data,macroeconomic data and so on.And it also expanded the data contents,such as the indoor and outdoor data,the overground and underground data,panoramic data and real data.It also introduced the contents of historical geographical information in different periods and real-time location information,address information of sensing equipment,real-time perception and interpreting information.It has overcome the difficulties of real-time access of Internet of Things(IoT)perception,multi-node collaboration,64-bit support,cluster deployment and has the characteristics of spatio-temporal management,ondemand service,large data analysis and micro-service architecture.It built spatio-temporal information big data center and spatio-temporal information cloud platform,realized the convergence and management of the distributed big data,deeply applied for land,transportation,environmental protection,police and subdistrict five areas,by supporting the integrated application of multi-source information and supporting intelligent deep application.In the aspect of hardware environment construction,according to the top-level design and unified arrangement of Smart Weifang,the WFCP was migrated to Weifang cloud computing center,to achieve the on-demand computing resources and dynamic scheduling load-based computing resources,to support the generalizing load map application.

大数据时代空间信息与数字技术专业建设探讨

面对大数据时代“空间信息与数字技术”专业建设的挑战,如何适应经济社会发展的人才需求,培养兼具“信息技术”+“领域知识”的“空间信息与数字技术”专业的复合型人才,是该专业持续建设需要着重探讨的问题。从创新“3332”人才培养模式、重构“信息技术+领域知识”的课程体系、构建“基础-综合-创新”相结合的实践教学体系三方面对该专业的建设问题进行了探讨,给出了与之对应的专业建设的具体措施、路径与办法,以期为大数据时代“空间信息与数字技术”专业建设和人才培养提供参考,以满足国家战略对该专业高层次复合型人才培养的需求。

中国疾病预防控制中心信息系统整合共享实践分析

目的/意义推进政务信息系统整合共享,以降低政府治理成本,实现治理体系和治理能力现代化。方法/过程以中国疾病预防控制中心信息系统整合共享工作为例,详细阐述整合路径及工作成效,对整合共享的难点和对策展开分析和探讨。结果/结论基于全民健康信息化建设,明确“统筹布局,共建共享”的总体要求,探索以大数据技术为抓手,实现数据的融合与应用,构建数字疾控新发展格局。

城市中心区轨道交通站点步行接驳路径特征及优化

轨道交通站点步行接驳路径特征研究,对提升轨道交通承担率具有重要意义。基于百度时空大数据API,运用空间分析法、情景模拟法、视觉语义分割法,以天津市营口道站点为例,构建城市中心区轨道交通站点步行接驳路径特征分析方法,挖掘步行接驳路径特征并揭示成因,提出针对性的优化对策。研究表明,目标站点步行接驳效率南部高于北部,具体表现在路段重复率分布均匀度南部大于北部,差异路径分布数量北部多于南部,超过3/4路径的非直线系数偏高;除站点周边区域外,接驳路径品质西北部优于东南部,具体表现在西北部视觉出行环境建设最优,区域整体设施服务水平良好。根据步行接驳路径特征,提出路网结构调整、道路断面优化、出入口调整、绿化品质提升的优化对策。

大数据公共管理情境下的个人信息披露态度

文章在大数据公共管理背景下,以新冠疫情防控期间的个人信息披露态度为例,研究个人内在因素和外在环境因素对个人信息披露态度的影响,以期为建构大数据公共管理中个人信息和谐的“披露-采集”关系提供理论支撑。研究设计综合保护动机理论和调节定向理论,引入隐私顾虑作为中介变量,构建结构方程模型;通过问卷调查法,收集覆盖16个城市4,800份大规模样本进行分析。研究发现:自我效能负向影响隐私顾虑,主观规范正向影响隐私顾虑,隐私顾虑负向影响个人信息披露态度;促进定向个体比预防定向个体隐私顾虑水平更低,更倾向于支持公共管理中的个人信息披露,同时调节定向特质在自我效能与隐私顾虑之间具有显著调节效应。

大数据时代隐私权的刑法保护模式重构

大数据促进了生活的便捷,带动了新兴行业的高速发展,但超出合理边界的数据滥用行为,也对公民的隐私安全带来了现实威胁。伴随大数据时代的纵深发展,个人隐私的保护从传统个人本位向社会本位发生转变,隐私权的法益内涵在大数据时代应当进行重新审视。随着信息保护的日渐重视,隐私权的价值应与个人信息权一样受到同等重视,刑法应当为大数据时代隐私权的保护开辟新路径,采取直接保护与间接保护并行的双轨制保护模式,重构个人信息的刑法定义,引入“弱可识别性+隐私性”标准,同时继续加强针对隐私权犯罪的积极预防,通过刑法调整来应对当前公民隐私权被侵害的现实。

勒索软件对科技信息安全的影响研究

在国家促进数据安全产业发展的新环境下,科技信息在其数字化转型过程中面临勒索软件的威胁。从科技信息管理者的角度分析勒索软件攻击科技信息的成因,以及造成的区别于以往安全威胁的后果。依据数据安全与网络安全的相关理论和技术,分析被勒索软件Mallox攻击过程中产生的28597条相关日志数据和121539个被加密文件,从而了解Mallox的攻击及加密流程,提出预防勒索软件的方法:自查向互联网开放的服务和网络安全策略,评估服务器被入侵的风险;在日常监测中关注ID 4625的日志数量和安全设备日志,可识别攻击过程;采用异地存储备份、部署EDR服务、使用VPN等方式提高科技信息的安全性。

基于大数据技术的企业信息资源管理系统

企业信息资源管理系统面对大规模和多类型数据涌入时,处理效率低,系统容易崩溃,设计基于大数据技术的企业信息资源管理系统。分析Hadoop平台体系结构,确定资源管理系统整体架构,设计每个子系统对应的功能,部署系统硬件环境;利用大数据融合技术设置软件执行算法,通过模糊调度挖掘资源间关联规则,根据相似性函数,建立同类资源的融合模型,提高业务处理效率,完成系统整体设计。实验表明,该系统的CPU利用率仅在30%左右,处理时间在1.00s以内,大大缩短了处理时间,提高了处理效率。