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Data Inference:Data Security Threats in the AI Era 认领 引用
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作者 Zijun Wang Ting Liu +2 位作者 Yang Liu Enrico Zio Xiaohong Guan 《Engineering》 SCIE EI CSCD 2025年第9期29-33,共5页
1.Introduction Data inference(DInf)is a data security threat in which critical information is inferred from low-sensitivity data.Once regarded as an advanced professional threat limited to intelligence analysts,DInf h... 1.Introduction Data inference(DInf)is a data security threat in which critical information is inferred from low-sensitivity data.Once regarded as an advanced professional threat limited to intelligence analysts,DInf has become a widespread risk in the artificial intelligence(AI)era. 展开更多
关键词 data security threats data security threat artificial intelligence ai era artificial intelligence data inference data inference dinf advanced professional threat
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A Lightweight IoT Data Security Sharing Scheme Based on Attribute-Based Encryption and Blockchain 认领 引用 被引量：2
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作者 Hongliang Tian Meiruo Li 《Computers, Materials & Continua》 SCIE EI 2025年第6期5539-5559,共21页
The accelerated advancement of the Internet of Things(IoT)has generated substantial data,including sensitive and private information.Consequently,it is imperative to guarantee the security of data sharing.While facili... The accelerated advancement of the Internet of Things(IoT)has generated substantial data,including sensitive and private information.Consequently,it is imperative to guarantee the security of data sharing.While facilitating fine-grained access control,Ciphertext Policy Attribute-Based Encryption(CP-ABE)can effectively ensure the confidentiality of shared data.Nevertheless,the conventional centralized CP-ABE scheme is plagued by the issues of keymisuse,key escrow,and large computation,which will result in security risks.This paper suggests a lightweight IoT data security sharing scheme that integrates blockchain technology and CP-ABE to address the abovementioned issues.The integrity and traceability of shared data are guaranteed by the use of blockchain technology to store and verify access transactions.The encryption and decryption operations of the CP-ABE algorithm have been implemented using elliptic curve scalarmultiplication to accommodate lightweight IoT devices,as opposed to themore arithmetic bilinear pairing found in the traditional CP-ABE algorithm.Additionally,a portion of the computation is delegated to the edge nodes to alleviate the computational burden on users.A distributed key management method is proposed to address the issues of key escrow andmisuse.Thismethod employs the edge blockchain to facilitate the storage and distribution of attribute private keys.Meanwhile,data security sharing is enhanced by combining off-chain and on-chain ciphertext storage.The security and performance analysis indicates that the proposed scheme is more efficient and secure. 展开更多
关键词 Edge blockchain CP-ABE data security sharing IoT
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Data Security and Privacy for AI-Enabled Smart Manufacturing 认领 引用
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作者 Xin Wang Daniel E.Quevedo +3 位作者 Dongrun Li Peng Cheng Jiming Chen Youxian Sun 《Engineering》 SCIE EI CSCD 2025年第9期34-39,共6页
1.Data security in smart manufacturing The global manufacturing sector is undergoing a digital transformation as traditional systems-reliant on physical assets such as raw materials and labor-struggle to meet demands ... 1.Data security in smart manufacturing The global manufacturing sector is undergoing a digital transformation as traditional systems-reliant on physical assets such as raw materials and labor-struggle to meet demands for greater flexibility and efficiency.The integration of advanced information technology facilitates smart manufacturing(SM),which optimizes production,management,and supply chains[1]. 展开更多
关键词 smart manufacturing data security smart manufacturing sm which ai enabled digital transformation advanced information technology privacy
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Adaptive Attribute-Based Honey Encryption: A Novel Solution for Cloud Data Security 认领 引用 被引量：1
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作者 Reshma Siyal Muhammad Asim +4 位作者 Long Jun Mohammed Elaffendi Sundas Iftikhar Rana Alnashwan Samia Allaoua Chelloug 《Computers, Materials & Continua》 SCIE EI 2025年第2期2637-2664,共28页
A basic procedure for transforming readable data into encoded forms is encryption, which ensures security when the right decryption keys are used. Hadoop is susceptible to possible cyber-attacks because it lacks built... A basic procedure for transforming readable data into encoded forms is encryption, which ensures security when the right decryption keys are used. Hadoop is susceptible to possible cyber-attacks because it lacks built-in security measures, even though it can effectively handle and store enormous datasets using the Hadoop Distributed File System (HDFS). The increasing number of data breaches emphasizes how urgently creative encryption techniques are needed in cloud-based big data settings. This paper presents Adaptive Attribute-Based Honey Encryption (AABHE), a state-of-the-art technique that combines honey encryption with Ciphertext-Policy Attribute-Based Encryption (CP-ABE) to provide improved data security. Even if intercepted, AABHE makes sure that sensitive data cannot be accessed by unauthorized parties. With a focus on protecting huge files in HDFS, the suggested approach achieves 98% security robustness and 95% encryption efficiency, outperforming other encryption methods including Ciphertext-Policy Attribute-Based Encryption (CP-ABE), Key-Policy Attribute-Based Encryption (KB-ABE), and Advanced Encryption Standard combined with Attribute-Based Encryption (AES+ABE). By fixing Hadoop’s security flaws, AABHE fortifies its protections against data breaches and enhances Hadoop’s dependability as a platform for processing and storing massive amounts of data. 展开更多
关键词 Cybersecurity data security cloud storage hadoop encryption and decryption privacy protection attribute-based honey encryption
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A Data Security Framework for Cloud Computing Services 认领 引用 被引量：4
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作者 Luis-Eduardo Bautista-Villalpando Alain Abran 《Computer Systems Science & Engineering》 SCIE EI 2021年第5期203-218,共16页
Cyberattacks are difficult to prevent because the targeted companies and organizations are often relying on new and fundamentally insecure cloudbased technologies,such as the Internet of Things.With increasing industr... Cyberattacks are difficult to prevent because the targeted companies and organizations are often relying on new and fundamentally insecure cloudbased technologies,such as the Internet of Things.With increasing industry adoption and migration of traditional computing services to the cloud,one of the main challenges in cybersecurity is to provide mechanisms to secure these technologies.This work proposes a Data Security Framework for cloud computing services(CCS)that evaluates and improves CCS data security from a software engineering perspective by evaluating the levels of security within the cloud computing paradigm using engineering methods and techniques applied to CCS.This framework is developed by means of a methodology based on a heuristic theory that incorporates knowledge generated by existing works as well as the experience of their implementation.The paper presents the design details of the framework,which consists of three stages:identification of data security requirements,management of data security risks and evaluation of data security performance in CCS. 展开更多
关键词 Cloud computing services computer security data security data security requirements data risk data security measurement
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Financial Data Security Management in the Era of Big Data 认领 引用
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作者 Yanling Liu Yun Li 《Proceedings of Business and Economic Studies》 2025年第2期37-42,共6页
In the era of big data,the financial industry is undergoing profound changes.By integrating multiple data sources such as transaction records,customer interactions,market trends,and regulatory requirements,big data te... In the era of big data,the financial industry is undergoing profound changes.By integrating multiple data sources such as transaction records,customer interactions,market trends,and regulatory requirements,big data technology has significantly improved the decision-making efficiency,customer insight,and risk management capabilities of financial institutions.The financial industry has become a pioneer in the application of big data technology,which is widely used in scenarios such as fraud detection,risk management,customer service optimization,and smart transactions.However,financial data security management also faces many challenges,including data breaches,privacy protection,compliance requirements,the complexity of emerging technologies,and the balance between data access and security.This article explores the major challenges of financial data security management,coping strategies,and the evolution of the regulatory environment,and it looks ahead to future trends,highlighting the important role of artificial intelligence and machine learning in financial data security. 展开更多
关键词 Big data Artificial intelligence Data security Privacy protection
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Data security governance in the era of big data: status, challenges, and prospects 认领 引用 被引量：28
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作者 Liyuan Sun Hongyun Zhang Chao Fang 《Data Science and Management》 2021年第2期41-44,共4页
At present,the global big data industry is experiencing vibrant development with technological evolution and application innovation speeding up side by side.Novel technologies for data storage,computing and analytics,... At present,the global big data industry is experiencing vibrant development with technological evolution and application innovation speeding up side by side.Novel technologies for data storage,computing and analytics,such as non-relational databases,distributed parallel computing,machine learning,and deep data mining have emerged and evolved rapidly.Big data has gradually become a fundamental national strategic resource and a cornerstone factor in production.However,increasing big data security problems are exposed and the number of big data security incidents occurring around the world is on the rise.Against this background,this paper provides a summary of the present situation of global data security governance,points out the challenges,and then proceeds to raise solutions for further modernizing data security governance systems. 展开更多
关键词 Data security Cyber security Data sharing Data privacy Big data
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Data Security Storage Model of the Internet of Things Based on Blockchain 认领 引用 被引量：5
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作者 Pingshui Wang Willy Susilo 《Computer Systems Science & Engineering》 SCIE EI 2021年第1期213-224,共12页
With the development of information technology,the Internet of Things(IoT)has gradually become the third wave of the worldwide information industry revolution after the computer and the Internet.The application of the... With the development of information technology,the Internet of Things(IoT)has gradually become the third wave of the worldwide information industry revolution after the computer and the Internet.The application of the IoT has brought great convenience to people’s production and life.However,the potential information security problems in various IoT applications are gradually exposed and people pay more attention to them.The traditional centralized data storage and management model of the IoT is easy to cause transmission delay,single point of failure,privacy disclosure and other problems,and eventually leads to unpredictable behavior of the system.Blockchain technology can effectively improve the operation and data security status of the IoT.Referring to the storage model of the Fabric blockchain project,this paper designs a data security storage model suitable for the IoT system.The simulation results show that the model is not only effective and extensible,but also can better protect the data security of the Internet of Things. 展开更多
关键词 Internet of Things(IoT) blockchain data security digital signatures encryption model
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Data Security and Privacy in Cloud Storage 认领 引用
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作者 Xinhua Dong Ruixuan Li +2 位作者 Wanwan Zhou Dongjie Liao Shuoyi Zhao 《ZTE Communications》 2013年第2期18-23,共6页
In this paper, we survey data security and privacy problems created by cloud storage applications and propose a cloud storage security architecture. We discuss state-of-the-art techniques for ensuring the privacy and ... In this paper, we survey data security and privacy problems created by cloud storage applications and propose a cloud storage security architecture. We discuss state-of-the-art techniques for ensuring the privacy and security of data stored in the cloud. We discuss policies for access control and data integrity, availability, and privacy. We also discuss several key solutions proposed in current literature and point out future research directions. 展开更多
关键词 cloud storage cloud computing data security privacy-preserving
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Protecting Data Mobility in Cloud Networks Using Metadata Security 认领 引用 被引量：1
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作者 R.Punithavathi M.Kowsigan +3 位作者 R.Shanthakumari Miodrag Zivkovic Nebojsa Bacanin Marko Sarac 《Computer Systems Science & Engineering》 SCIE EI 2022年第7期105-120,共16页
At present,health care applications,government services,and banking applications use big data with cloud storage to process and implement data.Data mobility in cloud environments uses protection protocols and algorith... At present,health care applications,government services,and banking applications use big data with cloud storage to process and implement data.Data mobility in cloud environments uses protection protocols and algorithms to secure sensitive user data.Sometimes,data may have highly sensitive information,lead-ing users to consider using big data and cloud processing regardless of whether they are secured are not.Threats to sensitive data in cloud systems produce high risks,and existing security methods do not provide enough security to sensitive user data in cloud and big data environments.At present,several security solu-tions support cloud systems.Some of them include Hadoop Distributed File Sys-tem(HDFS)baseline Kerberos security,socket layer-based HDFS security,and hybrid security systems,which have time complexity in providing security inter-actions.Thus,mobile data security algorithms are necessary in cloud environ-ments to avoid time risks in providing security.In our study,we propose a data mobility and security(DMoS)algorithm to provide security of data mobility in cloud environments.By analyzing metadata,data are classified as secured and open data based on their importance.Secured data are sensitive user data,whereas open data are open to the public.On the basis of data classification,secured data are applied to the DMoS algorithm to achieve high security in HDFS.The pro-posed approach is compared with the time complexity of three existing algo-rithms,and results are evaluated. 展开更多
关键词 Data mobility data security cloud computing big data DMoS algorithm
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Classification of Big Data Security Based on Ontology Web Language 认领 引用
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作者 Alsadig Mohammed Adam Abdallah Amir Mohamed Talib 《Journal of Information Security》 2023年第1期76-91,共16页
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 (I... 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. 展开更多
关键词 Big Data Big Data Security Information Security Data Security Ontology Web Language Protégé
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Proof of Activity Protocol for IoMT Data Security 认领 引用 被引量：4
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作者 R.Rajadevi K.Venkatachalam +2 位作者 Mehedi Masud Mohammed A.AlZain Mohamed Abouhawwash 《Computer Systems Science & Engineering》 SCIE EI 2023年第1期339-350,共12页
The Internet of Medical Things(IoMT)is an online device that senses and transmits medical data from users to physicians within a time interval.In,recent years,IoMT has rapidly grown in the medicalfield to provide heal... The Internet of Medical Things(IoMT)is an online device that senses and transmits medical data from users to physicians within a time interval.In,recent years,IoMT has rapidly grown in the medicalfield to provide healthcare services without physical appearance.With the use of sensors,IoMT applications are used in healthcare management.In such applications,one of the most important factors is data security,given that its transmission over the network may cause obtrusion.For data security in IoMT systems,blockchain is used due to its numerous blocks for secure data storage.In this study,Blockchain-assisted secure data management framework(BSDMF)and Proof of Activity(PoA)protocol using malicious code detection algorithm is used in the proposed data security for the healthcare system.The main aim is to enhance the data security over the networks.The PoA protocol enhances high security of data from the literature review.By replacing the malicious node from the block,the PoA can provide high security for medical data in the blockchain.Comparison with existing systems shows that the proposed simulation with BSD-Malicious code detection algorithm achieves higher accuracy ratio,precision ratio,security,and efficiency and less response time for Blockchain-enabled healthcare systems. 展开更多
关键词 Blockchain IoMT malicious code detection security secure data management framework data management PoA
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Towards Developing Privacy-Preserved Data Security Approach(PP-DSA)in Cloud Computing Environment 认领 引用
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作者 S.Stewart Kirubakaran V.P.Arunachalam +1 位作者 S.Karthik S.K annan 《Computer Systems Science & Engineering》 SCIE EI 2023年第3期1881-1895,共15页
In the present scenario of rapid growth in cloud computing models,several companies and users started to share their data on cloud servers.However,when the model is not completely trusted,the data owners face several ... In the present scenario of rapid growth in cloud computing models,several companies and users started to share their data on cloud servers.However,when the model is not completely trusted,the data owners face several security-related problems,such as user privacy breaches,data disclosure,data corruption,and so on,during the process of data outsourcing.For addressing and handling the security-related issues on Cloud,several models were proposed.With that concern,this paper develops a Privacy-Preserved Data Security Approach(PP-DSA)to provide the data security and data integrity for the out-sourcing data in Cloud Environment.Privacy preservation is ensured in this work with the Efficient Authentication Technique(EAT)using the Group Signature method that is applied with Third-Party Auditor(TPA).The role of the auditor is to secure the data and guarantee shared data integrity.Additionally,the Cloud Service Provider(CSP)and Data User(DU)can also be the attackers that are to be handled with the EAT.Here,the major objective of the work is to enhance cloud security and thereby,increase Quality of Service(QoS).The results are evaluated based on the model effectiveness,security,and reliability and show that the proposed model provides better results than existing works. 展开更多
关键词 Third-party auditor(TPA) efficient auditing technique(EAT) cloud service provider(CSP) data user(DU) data security privacy-preserving cloud computing cloud security
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Block Level Data Integrity Assurance Using Matrix Dialing Method towards High Performance Data Security on Cloud Storage 认领 引用
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作者 P. Premkumar D. Shanthi 《Circuits and Systems》 2016年第11期3626-3644,共19页
Data outsourcing through cloud storage enables the users to share on-demand resources with cost effective IT services but several security issues arise like confidentiality, integrity and authentication. Each of them ... Data outsourcing through cloud storage enables the users to share on-demand resources with cost effective IT services but several security issues arise like confidentiality, integrity and authentication. Each of them plays an important role in the successful achievement of the other. In cloud computing data integrity assurance is one of the major challenges because the user has no control over the security mechanism to protect the data. Data integrity insures that data received are the same as data stored. It is a result of data security but data integrity refers to validity and accuracy of data rather than protect the data. Data security refers to protection of data against unauthorized access, modification or corruption and it is necessary to ensure data integrity. This paper proposed a new approach using Matrix Dialing Method in block level to enhance the performance of both data integrity and data security without using Third Party Auditor (TPA). In this approach, the data are partitioned into number of blocks and each block converted into a square matrix. Determinant factor of each matrix is generated dynamically to ensure data integrity. This model also implements a combination of AES algorithm and SHA-1 algorithm for digital signature generation. Data coloring on digital signature is applied to ensure data security with better performance. The performance analysis using cloud simulator shows that the proposed scheme is highly efficient and secure as it overcomes the limitations of previous approaches of data security using encryption and decryption algorithms and data integrity assurance using TPA due to server computation time and accuracy. 展开更多
关键词 Cloud Computing Data Integrity Data Security SHA-1 Digital Signature AES Encryption and Decryption
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Research and practice of marine scientific data security governance 认领 引用
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作者 FU Yu TONG Xin +2 位作者 CAO Shengwen XU Mogeng HE Long 《Marine Science Bulletin》 2024年第2期69-83,共15页
at present,data security has become the most urgent and primary issue in the era of digital economy.Marine scientific data security is the most urgent core issue of marine data resource management and sharing service.... at present,data security has become the most urgent and primary issue in the era of digital economy.Marine scientific data security is the most urgent core issue of marine data resource management and sharing service.This paper focuses on the analysis of the needs of marine scientific data security governance,in-depth development of marine scientific data security governance approaches and methods,and practical application in the national marine scientific data center,optimizing the existing data management model,ensuring the safety of marine scientific data,and fully releasing the data value. 展开更多
关键词 data security governance marine scientific data data sharing
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PRACTICE,BREAKTHROUGHS AND IMPROVEMENT PATHWAYS OF CHINA'S DATA SECURITY REGULATION FROM THE PERSPECTIVE OF RISK REGULATION 认领 引用
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作者 Wang Shaotang 《China Legal Science》 2026年第2期107-131,共25页
Data security has emerged as a central topic in national governance in the digital age.As data becomes a new factor of production,its penetration into the economic,social,and political spheres continues to deepen,and ... Data security has emerged as a central topic in national governance in the digital age.As data becomes a new factor of production,its penetration into the economic,social,and political spheres continues to deepen,and the types and forms of data security risks have become more complex than ever before.In this regard,General Secretary Xi Jinping once pointed out that'The development of data-related fundamental institutions is crucial to the overall situation of national development and security.It is necessary to safeguard national data security,protect personal information and trade secrets,and promote the efficient circulation and utilization of data to empower the real economy. 展开更多
关键词 production factor risk regulation economic sphere data security risks data security social sphere national governance political sphere
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Hardware-enhanced data security for Internet of Things 认领 引用
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作者 Yingjie Xia Yizhao Zhou +4 位作者 Jia-Nan Liu Xuejiao Liu Jiming Chen Fu Xiao Haojin Zhu 《Fundamental Research》 CSCD 2026年第2期1086-1090,共5页
The Internet of Things(IoT)has become deeply embedded in daily life,enabling pervasive collection of user and environmental data.However,securing sensor-edge-cloud IoT architectures remains a critical challenge due to... The Internet of Things(IoT)has become deeply embedded in daily life,enabling pervasive collection of user and environmental data.However,securing sensor-edge-cloud IoT architectures remains a critical challenge due to their exposure to open environments,resource-constrained devices,and stringent requirements for high throughput and real-time responsiveness.While existing software-centric security solutions inadequately address these constraints,we propose a hardware-enhanced security framework that embeds specialized security chips across all IoT layers.At the sensor layer,the privacy-enabled analog-to-digital converter(ADC)integrates lightweight encryption during data acquisition to prevent raw sensitive data exposure.Edge nodes employ dedicated authentication microcontroller units(MCUs)to validate data integrity and source legitimacy under high concurrency.Cloud servers utilize cryptographic system-on-chip(SoC)modules for hardware-accelerated encryption and finegrained access control,enabling secure storage and dynamic policy enforcement.By shifting security primitives to hardware,this framework inherently mitigates software vulnerabilities,sustains high-throughput data processing,and minimizes reliance on resource-intensive software stacks,thereby establishing a robust foundation for next-generation IoT systems. 展开更多
关键词 Internet of Things(IoT) Hardware-enhanced Data security System-on-chip(SOC) Authorization
Data grading for intelligent connected vehicles:An inference strength-driven framework 认领 引用
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作者 Shichun Yang Bowen Zheng +9 位作者 Yi Shi Haoran Guang Tianyang Gong Weifeng Gong Xinjie Feng Mingjie Chen Yaoguang Cao Hao Wu Tian Liu Jia Zhao 《Digital Communications and Networks》 SCIE EI CSCD 2026年第4期698-715,共18页
Intelligent Connected Vehicles(ICVs)generate massive heterogeneous multi-modal data during operation,and due to the limited computing resources on board,graded data encryption protection is of great significance for b... Intelligent Connected Vehicles(ICVs)generate massive heterogeneous multi-modal data during operation,and due to the limited computing resources on board,graded data encryption protection is of great significance for balancing data security and efficient utilization.However,the current data grading processes struggle to address the evolving inference attacks and dynamic operational environments,and traditional grading approaches relying on static expert judgment or information-theoretic metrics.To bridge this gap,this paper proposes a novel inference strength-driven data grading framework,where inference strength quantifies the susceptibility of one dataset to infer another through adversarial reasoning.The framework employs a systematic methodology combining graph theory,optimization,and Large Language Models to construct an inference library and calculate inference strength.The framework also provides a PageRank-based algorithm to generate interpretable data grading lists for both static policy and vehicle-end application,prioritizing core data protection while respecting computational constraints.Validated on the Audi A2D2 dataset and real vehicle controller,our approach demonstrates improved protection utility compared to default grading baselines.The results highlight its potential to enhance data security in ICVs through prioritized protection of core data under computational constraints. 展开更多
关键词 Intelligent vehicles Data security Risk analysis Data handling
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Cloud data security with deep maxout assisted data sanitization and restoration process 认领 引用
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作者 Shrikant D.Dhamdhere M.Sivakkumar V.Subramanian 《High-Confidence Computing》 EI CSCD 2025年第1期68-81,共14页
The potential of cloud computing,an emerging concept to minimize the costs associated with computing has recently drawn the interest of a number of researchers.The fast advancements in cloud computing techniques led t... The potential of cloud computing,an emerging concept to minimize the costs associated with computing has recently drawn the interest of a number of researchers.The fast advancements in cloud computing techniques led to the amazing arrival of cloud services.But data security is a challenging issue for modern civilization.The main issues with cloud computing are cloud security as well as effective cloud distribution over the network.Increasing the privacy of data with encryption methods is the greatest approach,which has highly progressed in recent times.In this aspect,sanitization is also the process of confidentiality of data.The goal of this work is to present a deep learning-assisted data sanitization procedure for data security.The proposed data sanitization process involves the following steps:data preprocessing,optimal key generation,deep learning-assisted key fine-tuning,and Kronecker product.Here,the data preprocessing considers original data as well as the extracted statistical feature.Key generation is the subsequent process,for which,a self-adaptive Namib beetle optimization(SANBO)algorithm is developed in this research.Among the generated keys,appropriate keys are fine-tuned by the improved Deep Maxout classifier.Then,the Kronecker product is done in the sanitization process.Reversing the sanitization procedure will yield the original data during the data restoration phase.The study part notes that the suggested data sanitization technique guarantees cloud data security against malign attacks.Also,the analysis of proposed work in terms of restoration effectiveness and key sensitivity analysis is also done. 展开更多
关键词 Adopted data sanitization Cloud data security Restoration Improved deep maxout Optimal key generation
SEIZING THE OPPORTUNITIES OF GOOD GOVERNANCE UNDER THE RULE OF LAW,AND ENJOYING THE ACHIEVEMENTS OF THE DIGITAL DEVELOPMENT:CTRIP'S BUSINESS PRACTICES IN DATA SECURITY AND PRIVACY PROTECTION 认领 引用
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作者 Wang Xin Liu Yan 《China Legal Science》 2025年第2期33-39,共7页
Ctrip was founded in Shanghai in 1999.Thanks to the rapid development of China's Internet and the continuous improvement in cyberspace rule of law,Ctrip has seized the opportunity to thrive in the online travel in... Ctrip was founded in Shanghai in 1999.Thanks to the rapid development of China's Internet and the continuous improvement in cyberspace rule of law,Ctrip has seized the opportunity to thrive in the online travel industry and has been constantly developing in a standardized manner.In recent years,with revolutionary innovations in the field of artificial intelligence and the high-quality development of cyberspace rule of law that provides strong protection,Ctrip and other Internet enterprises have been able to fully focus on breaking through new technologies,planning new scenarios,providing new services,and achieving new developments.We have reflected on personal information and data systems. 展开更多
关键词 data security cyberspace rule lawctrip good governance developing standardized mannerin digital development artificial intelligence privacy protection
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