An improved cloud data auditing scheme with enhanced security based on trusted execution environment-based multi-replica data audit

Cloud computing is an internet based model that enable convenient, on demand and pay per use access to a pool of shared resources. It is a new technology that satisfies a user's requirement for computing resources like networks, storage, servers, services and applications, Data security is one of the leading concerns and primary challenges for cloud computing. This issue is getting more serious with the development of cloud computing. From the consumers' perspective, cloud computing security concerns, especially data security and privacy protection issues, remain the primary inhibitor for adoption of cloud computing services. This paper analyses the basic problem of cloud computing and describes the data security and privacy protection issues in cloud.

It is well-known that cloud computing has many potential advantages and many enterprise applications and data are migrating to public or hybrid cloud. But regarding some business-critical applications, the organizations, especially large enterprises, still wouldn't move them to cloud. The market size the cloud computing shared is still far behind the one expected. From the consumers' perspective, cloud computing security concerns, especially data security and privacy protection issues, remain the primary inhibitor for adoption of cloud computing services. This paper provides a concise but all-round analysis on data security and privacy protection issues associated with cloud computing across all stages of data life cycle. Then this paper discusses some current solutions. Finally, this paper describes future research work about data security and privacy protection issues in cloud.

Data security has consistently been a major issue in information technology. In the cloud computing environment, it becomes particularly serious because the data is located in different places even in all the globe. Data security and privacy protection are the two main factors of user's concerns about the cloud technology. Though many techniques on the topics in cloud computing have been investigated in both academics and industries, data security and privacy protection are becoming more important for the future development of cloud computing technology in government, industry, and business. Data security and privacy protection issues are relevant to both hardware and software in the cloud architecture. This study is to review different security techniques and challenges from both software and hardware aspects for protecting data in the cloud and aims at enhancing the data security and privacy protection for the trustworthy cloud environment. In this paper, we make a comparative research analysis of the existing research work regarding the data security and privacy protection techniques used in the cloud computing.

Data security has consistently been a major issue in information technology. In the cloud computing environment, it becomes particularly serious because the data is located in different places even in all the globe. Data security and privacy protection are the two main factors of user’s concerns about the cloud technology. Though many techniques on the topics in cloud computing have been investigated in both academics and industries, data security and privacy protection are becoming more important for the future development of cloud computing technology in government, industry, and business. Data security and privacy protection issues are relevant to both hardware and software in the cloud architecture. This study is to review different security techniques and challenges from both software and hardware aspects for protecting data in the cloud and aims at enhancing the data security and privacy protection for the trustworthy cloud environment. In this paper, we make a comparative research analysis of the existing research work regarding the data security and privacy protection techniques used in the cloud computing

Artificial intelligence (AI) and deep learning algorithms are advancing rapidly, with these emerging technologies being widely applied in areas such as audio-visual recognition and natural language processing. However, in recent years, researchers have identified several security risks in current mainstream AI models, which could hinder the further development of AI technologies. As a result, the issues of data security and privacy protection in AI models have become a focus of research. The data and privacy leakage problems are primarily studied from two perspectives: data leakage based on model outputs and data leakage based on model updates. In the context of model output-based data leakage, the study discusses the principles and research status of model theft attacks, model inversion attacks, and membership inference attacks. In the context of model update-based data leakage, the research focuses on how attackers can steal private data during the distributed training process. Regarding data and privacy protection, three common defense methods are primarily studied: model structure defenses, information obfuscation defenses, and query control defenses. This paper reviews the cutting-edge research achievements in the field of data security and privacy protection in AI deep learning models, focusing on the theoretical foundations, key findings, and related applications of data theft and defense technologies in AI deep learning models. Keywords: Artificial Intelligence, Data Security, Privacy Leakage, Privacy Protection

The new development trends including Internet of Things (IoT), smart city, enterprises digital transformation and world's digital economy are at the top of the tide. The continuous growth of data storage pressure drives the rapid development of the entire storage market on account of massive data generated. By providing data storage and management, cloud storage system becomes an indispensable part of the new era. Currently, the governments, enterprises and individual users are actively migrating their data to the cloud. Such a huge amount of data can create magnanimous wealth. However, this increases the possible risk, for instance, unauthorized access, data leakage, sensitive information disclosure and privacy disclosure. Although there are some studies on data security and privacy protection, there is still a lack of systematic surveys on the subject in cloud storage system. In this paper, we make a comprehensive review of the literatures on data security and privacy issues, data encryption technology, and applicable countermeasures in cloud storage system. Specifically, we first make an overview of cloud storage, including definition, classification, architecture and applications. Secondly, we give a detailed analysis on challenges and requirements of data security and privacy protection in cloud storage system. Thirdly, data encryption technologies and protection methods are summarized. Finally, we discuss several open research topics of data security for cloud storage.

The development of artificial intelligence has brought unprecedented opportunities for security education in universities. The author combines artificial intelligence with security education to explore the feasibility and security measures of building a new ecosystem of Artificial Intelligence + Security Education. Artificial intelligence has unique advantages in developing intelligent security education courses, improving teaching methods, and enhancing teaching evaluation. While leveraging its advantages, there are also many challenges. One of them is the low degree of integration between education and information technology, mainly manifested in the large gap between teaching concepts and artificial intelligence basic technologies, and technological development is at a bottleneck stage. Additionally, as data security and privacy protection issues become increasingly prominent, it is necessary to establish sound data security protection measures and privacy protection systems to securely and effectively protect learners' personal privacy and behavioral data. Based on the above issues, the author proposes the following measures for improvement. First, the security education faculty team should be improved and the cultivation of educational work philosophy should be strengthened. Second, the security education system construction should be continually improved in accordance with data security and privacy protection requirements.

To study and solve the issues of big data security and privacy protection is the key to promote the healthy development of big data. In this paper, a 3D big data security and privacy protection model based on life cycle, security goal and user role is presented, and the security challenges and security goals of big data are analyzed from different aspects. The feasible technology and its application in big data security and privacy protection are expounded and analyzed, and the in-depth research direction of big data security and privacy protection is discussed and summarized.

Cloud Computing is a way to deal with fabricate the breaking point or incorporate capacities effectively without placing assets into new system, planning new staff, or approving new programming. They have various potential purposes of intrigue and various attempt applications and data are moving to open or half and half cloud. However, regarding some business-fundamental applications, the affiliations, especially considerable attempts, still wouldn't move them to cloud. The market gauge the Cloud Computing shared is as yet far behind the one expected. From the clients' perspective, Cloud Computing security concerns, especially data security and security confirmation issues, remain the basic inhibitor for gathering of Cloud Computing organizations. In this paper, we present a diagram on data security and security protection issues related with Cloud Computing over all periods of data life cycle. Firstly,, we moreover focus a related works and after that we show unpretentious components of Cloud Computing security issues, and a short time later data security and insurance affirmation issues, and a while later we display some present courses of action in cloud. Finally, we look at future work about data security and security affirmation issues in cloud. Keywords: cloud computing; security deployment; privacy protection infrastructure.

Cloud computing is the delivery of shared computing services—including servers, storage, databases, networking, software, analytics, and intelligence— over the Internet (“the cloud”) to offer faster innovation, flexible resources, and economies of scale. To simplify, Cloud computing is on-demand delivery of IT resources. Many organizations are stuck in the conundrum of whether to cloudify or not to cloudify, mainly due to concerns related to the security of enterprise sensitive data. Removing this barrier is the pre-requisite to fully unleash the tremendous potential of cloud computing. The revolutionary principle of ‘Shared Resources’ is the cause of concern from Information Security point of view. Confidentiality, Integrity, Availability, Authenticity, and Privacy are essential concerns for both Cloud providers and consumers as well. Security concerns have given rise to an active area of research due to the many security threats that many organizations have faced at present. This seminar report provides a concise study on data security and privacy protection issues associated with cloud computing. Then this report discusses some current solutions and finally describes some measures for top identified threats in data security and privacy protection issues in cloud.

With the known evidence that potential connected vehicle (CV) users are worried about sharing data because of the associated data privacy and security issues, this study investigates the importance of the reputation of the data manager (who collects, stores, and owns the data) of CV technology (CVT). Based on a questionnaire survey of 2400 US adults, this study asserts that the data manager's reputation has a significant impact on the public perception of data privacy and security issues in CVT along with overall CV acceptance. The results show that data privacy issues have a more negative impact on CV acceptance than data security issues. In addition, the reputation of a data manager has a bigger role in the eyes of the public in shaping their perception of data privacy in comparison to data security. Based on the results, the public considers data privacy as the responsibility of the data manager to protect their data from unauthorized/illegal third-party access, whereas data security is the technological strength of CVT to protect the data from hacking. Finally, it is recommended that CV stakeholders take actions to improve potential CV users' confidence in the privacy of data shared by disclosing the data management process, data privacy protection efforts, building public trust in the data manager, and introducing/enforcing laws regarding data privacy protection.

PDF HTML XML Export Cite reminder BDMasker: Dynamic Data Protection System for Open Big Data Environment DOI: 10.21655/ijsi.1673-7288.00297 Author: Affiliation: Clc Number: Fund Project: Article | Figures | Metrics | Reference | Related | Cited by | Materials | Comments Abstract:Big data has become a national basic strategic resource, and the opening and sharing of data is the core of China's big data strategy. Cloud native technology and lake-house architecture are reconstructing the big data infrastructure and promoting data sharing and value dissemination. The development of the big data industry and technology requires stronger data security and data sharing capabilities. However, data security in an open environment has become a bottleneck, which restricts the development and utilization of big data technology. The issues of data security and privacy protection have become increasingly prominent both in the open source big data ecosystem and the commercial big data system. Dynamic data protection system under the open big data environment is now facing challenges in regards such as data availability, processing efficiency, and system scalability. This paper proposes the dynamic data protection system BDMasker for the open big data environment. Through a precise query analysis and query rewriting technology based on the query dependency model, it can accurately perceive but does not change the original business request, which indicates that the whole process of dynamic masking has zero impact on the business. Furthermore, its multi-engine-oriented unified security strategy framework realizes the vertical expansion of dynamic data protection capabilities and the horizontal expansion among multiple computing engines. The distributed computing capability of the big data execution engine can be used to improve the data protection processing performance of the system. The experimental results show that the precise SQL analysis and rewriting technology proposed by BDMasker is effective. The system has good scalability and performance, and the overall performance fluctuates within 3% in the TPC-DS and YCSB benchmark tests. Reference Related Cited by

<p><em>Data security has consistently been a major issue in information technology. In the cloud computing environment, it becomes particularly serious because the data is located in different places even in all the globe. Data security and privacy protection are the two main factors of user’s concerns about the cloud technology. Though many techniques on the topics in cloud computing have been investigated in both academics and industries, data security and privacy protection are becoming more important for the</em></p> <p><em>future development of cloud computing technology in government, industry, and business. Data security and privacy protection issues are relevant to both hardware and software in the cloud architecture. This study is to review different security techniques and challenges from both software and hardware aspects for protecting data in the cloud and aims at enhancing the data security and privacy protection for the trustworthy cloud environment. In this paper, we make a comparative research analysis of the existing research work regarding the data security and privacy protection techniques used in the cloud computing.</em></p>

Build an Audit Framework for Data Privacy Protection in Cloud Environment

Aiming at the big data security and privacy protection issues in the smart grid, the current key technologies for big data security and privacy protection in smart grids are sorted out, and a privacy-protecting smart grid association rule is proposed according to the privacy-protecting smart grid big data analysis and mining technology route The mining plan specifically analyzes the risk factors in the operation of the new power grid, and discusses the information security of power grid users from the perspective of the user, focusing on the protection of privacy and security, using safe multi-party calculation of the support and confidence of the association rules. Privacy-protecting smart grid big data mining enables power companies to improve service quality to 7.5% without divulging customer private information.