Big data has ushered in an era of unprecedented access to vast amounts of new,unstructured data,particularly in the realm of sensitive information.It presents unique opportunities for enhancing risk alerting systems,b...Big data has ushered in an era of unprecedented access to vast amounts of new,unstructured data,particularly in the realm of sensitive information.It presents unique opportunities for enhancing risk alerting systems,but also poses challenges in terms of extraction and analysis due to its diverse file formats.This paper proposes the utilization of a DAE-based(Deep Auto-encoders)model for projecting risk associated with financial data.The research delves into the development of an indicator assessing the degree to which organizations successfully avoid displaying bias in handling financial information.Simulation results demonstrate the superior performance of the DAE algorithm,showcasing fewer false positives,improved overall detection rates,and a noteworthy 9%reduction in failure jitter.The optimized DAE algorithm achieves an accuracy of 99%,surpassing existing methods,thereby presenting a robust solution for sensitive data risk projection.展开更多
Numerous industries,especially the medical industry,are likely to exhibit significant developments in the future.Ever since the announcement of the precision medicine initiative by the United States in 2015,interest i...Numerous industries,especially the medical industry,are likely to exhibit significant developments in the future.Ever since the announcement of the precision medicine initiative by the United States in 2015,interest in the field has considerably increased.The techniques of precision medicine are employed to provide optimal treatment and medical services to patients,in addition to the prevention and management of diseases via the collection and analysis of big data related to their individual genetic characteristics,occupation,living environment,and dietary habits.As this involves the accumulation and utilization of sensitive information,such as patient history,DNA,and personal details,its implementation is difficult if the data are inaccurate,exposed,or forged,and there is also a concern for privacy,as massive amount of data are collected;hence,ensuring the security of information is essential.Therefore,it is necessary to develop methods of securely sharing sensitive data for the establishment of a precision medicine system.An authentication and data sharing scheme is presented in this study on the basis of an analysis of sensitive data.The proposed scheme securely shares sensitive data of each entity in the precision medicine system according to its architecture and data flow.展开更多
In the era of digital intelligence,data is a key element in promoting social and economic development.Educational data,as a vital component of data,not only supports teaching and learning but also contains much sensit...In the era of digital intelligence,data is a key element in promoting social and economic development.Educational data,as a vital component of data,not only supports teaching and learning but also contains much sensitive information.How to effectively categorize and protect sensitive data has become an urgent issue in educational data security.This paper systematically researches and constructs a multi-dimensional classification framework for sensitive educational data,and discusses its security protection strategy from the aspects of identification and desensitization,aiming to provide new ideas for the security management of sensitive educational data and to help the construction of an educational data security ecosystem in the era of digital intelligence.展开更多
The exponential advancement witnessed in 5G communication and quantum computing has presented unparalleled prospects for safeguarding sensitive data within healthcare infrastructures.This study proposes a novel framew...The exponential advancement witnessed in 5G communication and quantum computing has presented unparalleled prospects for safeguarding sensitive data within healthcare infrastructures.This study proposes a novel framework for healthcare applications that integrates 5G communication,quantum computing,and sensitive data measurement to address the challenges of measuring and securely transmitting sensitive medical data.The framework includes a quantum-inspired method for quantifying data sensitivity based on quantum superposition and entanglement principles and a delegated quantum computing protocol for secure data transmission in 5G-enabled healthcare systems,ensuring user anonymity and data confidentiality.The framework is applied to innovative healthcare scenarios,such as secure 5G voice communication,data transmission,and short message services.Experimental results demonstrate the framework’s high accuracy in sensitive data measurement and enhanced security for data transmission in 5G healthcare systems,surpassing existing approaches.展开更多
Users store vast amounts of sensitive data on a big data platform. Sharing sensitive data will help enterprises reduce the cost of providing users with personalized services and provide value-added data services.Howev...Users store vast amounts of sensitive data on a big data platform. Sharing sensitive data will help enterprises reduce the cost of providing users with personalized services and provide value-added data services.However, secure data sharing is problematic. This paper proposes a framework for secure sensitive data sharing on a big data platform, including secure data delivery, storage, usage, and destruction on a semi-trusted big data sharing platform. We present a proxy re-encryption algorithm based on heterogeneous ciphertext transformation and a user process protection method based on a virtual machine monitor, which provides support for the realization of system functions. The framework protects the security of users＇ sensitive data effectively and shares these data safely. At the same time, data owners retain complete control of their own data in a sound environment for modern Internet information security.展开更多
Sharing sensitive data is a specific challenge for research infrastructures in the field of life sciences.For that reason a toolbox has been developed,providing resources for researchers who wish to share and use sens...Sharing sensitive data is a specific challenge for research infrastructures in the field of life sciences.For that reason a toolbox has been developed,providing resources for researchers who wish to share and use sensitive data,to support the workflows for handling these kinds of digital objects.Common and community approved annotations are required to be compliant with FAIR principles(Findability,Accessibility,Interoperability,Reusability).The toolbox makes use of a tagging(categorisation)system,allowing consistent labelling and categorisation of digital objects,in terms relevant to data sharing tasks and activities.A pilot study was performed within the Horizon 2020 project EOSC-Life,in which 2 experts from 6 life sciences research infrastructures were recruited to independently assign tags to the same set of 10 to 25 resources related to sensitive data management and data sharing(in total 110).Summary statistics of agreement and observer variation per research infrastructure are provided.The pilot study has shown that experts were able to attribute tags but in most cases with a considerable observer variation between experts.In the context of CWFR(Canonical Workflow Frameworks for Research),this indicates the necessity for careful definition,evaluation and validation of parameters and processes related to workflow descriptions.The results from this pilot study were used to tackle this issue by revising the categorisation system and providing an updated version.展开更多
The elliptic curve cryptography algorithm represents a major advancement in the field of computer security. This innovative algorithm uses elliptic curves to encrypt and secure data, providing an exceptional level of ...The elliptic curve cryptography algorithm represents a major advancement in the field of computer security. This innovative algorithm uses elliptic curves to encrypt and secure data, providing an exceptional level of security while optimizing the efficiency of computer resources. This study focuses on how elliptic curves cryptography helps to protect sensitive data. Text is encrypted using the elliptic curve technique because it provides great security with a smaller key on devices with limited resources, such as mobile phones. The elliptic curves cryptography of this study is better than using a 256-bit RSA key. To achieve equivalent protection by using the elliptic curves cryptography, several Python libraries such as cryptography, pycryptodome, pyQt5, secp256k1, etc. were used. These technologies are used to develop a software based on elliptic curves. If built, the software helps to encrypt and decrypt data such as a text messages and it offers the authentication for the communication.展开更多
We focus on security and privacy problems within a cloud database framework,exploiting the DataBase as a Service(DBaaS).In this framework,an information proprietor drives out its information to a cloud database profes...We focus on security and privacy problems within a cloud database framework,exploiting the DataBase as a Service(DBaaS).In this framework,an information proprietor drives out its information to a cloud database professional company.The Data-Owner(DO)encrypts the delicate information before transmission at the cloud database professional company end to offer information security.Current encryption ideas,nonetheless,are just halfway homomorphic as all of them intend to enable an explicit kind of calculation,which is accomplished on scrambled information.These current plans can't be coordinated to solve genuine functional queries that include activities of various types.We propose and evaluate a Verifiable Reliable Secure-DataBase(VRS-DB)framework on shared tables along with many primary operations on scrambled information,which enables information interoperability,and permits an extensive possibility of Structured Query Language(SQL)queries to be prepared by the service provider on the encoded data.We show that our security and privacy idea is protected from two forms of threats and are fundamentally proficient.展开更多
Big data technologies have seen tremendous growth in recent years. They are widely used in both industry and academia. In spite of such exponential growth, these technologies lack adequate measures to protect data fro...Big data technologies have seen tremendous growth in recent years. They are widely used in both industry and academia. In spite of such exponential growth, these technologies lack adequate measures to protect data from misnse/abuse. Corporations that collect data from multiple sources are at risk of liabilities due to the exposure of sensitive information. In the current implementation of Hadoop, only file-level access control is feasible. Providing users with the ability to access data based on the attlibutes in a dataset or the user＇s role is complicated because of the sheer volume and multiple formats （structured, unstructured and semi-structured） of data. In this paper, we propose an access control framework, which enforces access control policies dynamically based on the sensitivity of the data. This framework enforces access control policies by harnessing the data context, usage patterns and informat/on sensitivity. Information sensitivity changes over time with the addition and removal of datasets, which can lead to modifications in access control decisions. The proposed framework accommodates these changes. The proposed framework is automated to a large extent as the data itself determines the sensitivity with minimal user intervention. Our experimental results show that the proposed framework is capable of enforcing access control policies on non-multimedia datasets with minimal overhead.展开更多
The present paper analyzes the hold and read stability with temperature and aspect ratio variations. To reduce the power dissipation, one of the effective techniques is the supply voltage reduction. At this reduced su...The present paper analyzes the hold and read stability with temperature and aspect ratio variations. To reduce the power dissipation, one of the effective techniques is the supply voltage reduction. At this reduced supply voltage the data must be stable. So, the minimum voltage should be discovered which can also retain the data. This voltage is the data retention voltage（DRV）. The DRV for 6T SRAM cell is estimated and analyzed in this paper.The sensitivity analysis is performed for the DRV variation with the variation in the temperature and aspect ratio of the pull up and pull down transistors. Cadence Virtuoso is used for DRV analysis using 45 nm GPDK technology files. After this, the read stability analysis of 6T SRAM cell in terms of SRRV（supply read retention voltage） and WRRV（wordline read retention voltage） is carried out. Read stability in terms of RSNM can be discovered by accessing the internal storage nodes. But in the case of dense SRAM arrays instead of using internal storage nodes,the stability can be discovered by using direct bit line measurements with the help of SRRV and WRRV. SRRV is used to find the minimum supply voltage for which data can be retained during a read operation. Similarly, WRRV is used to find the boosted value of wordline voltage, for which data can be retained during read operation. The SRRV and WRRV values are then analyzed for different Cell Ratios. The results of SRRV and WRRV are then compared with the reported data for the validation of the accuracy of the results.展开更多
文摘Big data has ushered in an era of unprecedented access to vast amounts of new,unstructured data,particularly in the realm of sensitive information.It presents unique opportunities for enhancing risk alerting systems,but also poses challenges in terms of extraction and analysis due to its diverse file formats.This paper proposes the utilization of a DAE-based(Deep Auto-encoders)model for projecting risk associated with financial data.The research delves into the development of an indicator assessing the degree to which organizations successfully avoid displaying bias in handling financial information.Simulation results demonstrate the superior performance of the DAE algorithm,showcasing fewer false positives,improved overall detection rates,and a noteworthy 9%reduction in failure jitter.The optimized DAE algorithm achieves an accuracy of 99%,surpassing existing methods,thereby presenting a robust solution for sensitive data risk projection.
文摘Numerous industries,especially the medical industry,are likely to exhibit significant developments in the future.Ever since the announcement of the precision medicine initiative by the United States in 2015,interest in the field has considerably increased.The techniques of precision medicine are employed to provide optimal treatment and medical services to patients,in addition to the prevention and management of diseases via the collection and analysis of big data related to their individual genetic characteristics,occupation,living environment,and dietary habits.As this involves the accumulation and utilization of sensitive information,such as patient history,DNA,and personal details,its implementation is difficult if the data are inaccurate,exposed,or forged,and there is also a concern for privacy,as massive amount of data are collected;hence,ensuring the security of information is essential.Therefore,it is necessary to develop methods of securely sharing sensitive data for the establishment of a precision medicine system.An authentication and data sharing scheme is presented in this study on the basis of an analysis of sensitive data.The proposed scheme securely shares sensitive data of each entity in the precision medicine system according to its architecture and data flow.
基金Education Science planning project of Jiangsu Province in 2024(Grant No:B-b/2024/01/152)2025 Jiangsu Normal University Graduate Research and Innovation Program school-level project“Research on the Construction and Desensitization Strategies of Education Sensitive Data Classification from the Perspective of Educational Ecology”。
文摘In the era of digital intelligence,data is a key element in promoting social and economic development.Educational data,as a vital component of data,not only supports teaching and learning but also contains much sensitive information.How to effectively categorize and protect sensitive data has become an urgent issue in educational data security.This paper systematically researches and constructs a multi-dimensional classification framework for sensitive educational data,and discusses its security protection strategy from the aspects of identification and desensitization,aiming to provide new ideas for the security management of sensitive educational data and to help the construction of an educational data security ecosystem in the era of digital intelligence.
文摘The exponential advancement witnessed in 5G communication and quantum computing has presented unparalleled prospects for safeguarding sensitive data within healthcare infrastructures.This study proposes a novel framework for healthcare applications that integrates 5G communication,quantum computing,and sensitive data measurement to address the challenges of measuring and securely transmitting sensitive medical data.The framework includes a quantum-inspired method for quantifying data sensitivity based on quantum superposition and entanglement principles and a delegated quantum computing protocol for secure data transmission in 5G-enabled healthcare systems,ensuring user anonymity and data confidentiality.The framework is applied to innovative healthcare scenarios,such as secure 5G voice communication,data transmission,and short message services.Experimental results demonstrate the framework’s high accuracy in sensitive data measurement and enhanced security for data transmission in 5G healthcare systems,surpassing existing approaches.
基金supported by the National Natural Science Foundation of China (Nos. 61173170, 61300222, 61433006, and U1401258)Independent Innovation Fund of Huazhong University of Science and Technology (Nos. 2012TS052, 2012TS053, 2013QN120, and CXY13Q019)
文摘Users store vast amounts of sensitive data on a big data platform. Sharing sensitive data will help enterprises reduce the cost of providing users with personalized services and provide value-added data services.However, secure data sharing is problematic. This paper proposes a framework for secure sensitive data sharing on a big data platform, including secure data delivery, storage, usage, and destruction on a semi-trusted big data sharing platform. We present a proxy re-encryption algorithm based on heterogeneous ciphertext transformation and a user process protection method based on a virtual machine monitor, which provides support for the realization of system functions. The framework protects the security of users＇ sensitive data effectively and shares these data safely. At the same time, data owners retain complete control of their own data in a sound environment for modern Internet information security.
基金funding from the European Union's Horizon 2020 research and innovation programme"EOSC-Life-Providing an open collaborative space for digital biology in Europe"under grant agreement No.824087.
文摘Sharing sensitive data is a specific challenge for research infrastructures in the field of life sciences.For that reason a toolbox has been developed,providing resources for researchers who wish to share and use sensitive data,to support the workflows for handling these kinds of digital objects.Common and community approved annotations are required to be compliant with FAIR principles(Findability,Accessibility,Interoperability,Reusability).The toolbox makes use of a tagging(categorisation)system,allowing consistent labelling and categorisation of digital objects,in terms relevant to data sharing tasks and activities.A pilot study was performed within the Horizon 2020 project EOSC-Life,in which 2 experts from 6 life sciences research infrastructures were recruited to independently assign tags to the same set of 10 to 25 resources related to sensitive data management and data sharing(in total 110).Summary statistics of agreement and observer variation per research infrastructure are provided.The pilot study has shown that experts were able to attribute tags but in most cases with a considerable observer variation between experts.In the context of CWFR(Canonical Workflow Frameworks for Research),this indicates the necessity for careful definition,evaluation and validation of parameters and processes related to workflow descriptions.The results from this pilot study were used to tackle this issue by revising the categorisation system and providing an updated version.
文摘The elliptic curve cryptography algorithm represents a major advancement in the field of computer security. This innovative algorithm uses elliptic curves to encrypt and secure data, providing an exceptional level of security while optimizing the efficiency of computer resources. This study focuses on how elliptic curves cryptography helps to protect sensitive data. Text is encrypted using the elliptic curve technique because it provides great security with a smaller key on devices with limited resources, such as mobile phones. The elliptic curves cryptography of this study is better than using a 256-bit RSA key. To achieve equivalent protection by using the elliptic curves cryptography, several Python libraries such as cryptography, pycryptodome, pyQt5, secp256k1, etc. were used. These technologies are used to develop a software based on elliptic curves. If built, the software helps to encrypt and decrypt data such as a text messages and it offers the authentication for the communication.
文摘We focus on security and privacy problems within a cloud database framework,exploiting the DataBase as a Service(DBaaS).In this framework,an information proprietor drives out its information to a cloud database professional company.The Data-Owner(DO)encrypts the delicate information before transmission at the cloud database professional company end to offer information security.Current encryption ideas,nonetheless,are just halfway homomorphic as all of them intend to enable an explicit kind of calculation,which is accomplished on scrambled information.These current plans can't be coordinated to solve genuine functional queries that include activities of various types.We propose and evaluate a Verifiable Reliable Secure-DataBase(VRS-DB)framework on shared tables along with many primary operations on scrambled information,which enables information interoperability,and permits an extensive possibility of Structured Query Language(SQL)queries to be prepared by the service provider on the encoded data.We show that our security and privacy idea is protected from two forms of threats and are fundamentally proficient.
文摘Big data technologies have seen tremendous growth in recent years. They are widely used in both industry and academia. In spite of such exponential growth, these technologies lack adequate measures to protect data from misnse/abuse. Corporations that collect data from multiple sources are at risk of liabilities due to the exposure of sensitive information. In the current implementation of Hadoop, only file-level access control is feasible. Providing users with the ability to access data based on the attlibutes in a dataset or the user＇s role is complicated because of the sheer volume and multiple formats （structured, unstructured and semi-structured） of data. In this paper, we propose an access control framework, which enforces access control policies dynamically based on the sensitivity of the data. This framework enforces access control policies by harnessing the data context, usage patterns and informat/on sensitivity. Information sensitivity changes over time with the addition and removal of datasets, which can lead to modifications in access control decisions. The proposed framework accommodates these changes. The proposed framework is automated to a large extent as the data itself determines the sensitivity with minimal user intervention. Our experimental results show that the proposed framework is capable of enforcing access control policies on non-multimedia datasets with minimal overhead.
文摘The present paper analyzes the hold and read stability with temperature and aspect ratio variations. To reduce the power dissipation, one of the effective techniques is the supply voltage reduction. At this reduced supply voltage the data must be stable. So, the minimum voltage should be discovered which can also retain the data. This voltage is the data retention voltage（DRV）. The DRV for 6T SRAM cell is estimated and analyzed in this paper.The sensitivity analysis is performed for the DRV variation with the variation in the temperature and aspect ratio of the pull up and pull down transistors. Cadence Virtuoso is used for DRV analysis using 45 nm GPDK technology files. After this, the read stability analysis of 6T SRAM cell in terms of SRRV（supply read retention voltage） and WRRV（wordline read retention voltage） is carried out. Read stability in terms of RSNM can be discovered by accessing the internal storage nodes. But in the case of dense SRAM arrays instead of using internal storage nodes,the stability can be discovered by using direct bit line measurements with the help of SRRV and WRRV. SRRV is used to find the minimum supply voltage for which data can be retained during a read operation. Similarly, WRRV is used to find the boosted value of wordline voltage, for which data can be retained during read operation. The SRRV and WRRV values are then analyzed for different Cell Ratios. The results of SRRV and WRRV are then compared with the reported data for the validation of the accuracy of the results.
