【Objective】Medical imaging data has great value,but it contains a significant amount of sensitive information about patients.At present,laws and regulations regarding to the de-identification of medical imaging data...【Objective】Medical imaging data has great value,but it contains a significant amount of sensitive information about patients.At present,laws and regulations regarding to the de-identification of medical imaging data are not clearly defined around the world.This study aims to develop a tool that meets compliance-driven desensitization requirements tailored to diverse research needs.【Methods】To enhance the security of medical image data,we designed and implemented a DICOM format medical image de-identification system on the Windows operating system.【Results】Our custom de-identification system is adaptable to the legal standards of different countries and can accommodate specific research demands.The system offers both web-based online and desktop offline de-identification capabilities,enabling customization of de-identification rules and facilitating batch processing to improve efficiency.【Conclusions】This medical image de-identification system robustly strengthens the stewardship of sensitive medical data,aligning with data security protection requirements while facilitating the sharing and utilization of medical image data.This approach unlocks the intrinsic value inherent in such datasets.展开更多
As technology develops,the amount of information being used has increased a lot.Every company learns big data to provide customized services with its customers.Accordingly,collecting and analyzing data of the data sub...As technology develops,the amount of information being used has increased a lot.Every company learns big data to provide customized services with its customers.Accordingly,collecting and analyzing data of the data subject has become one of the core competencies of the companies.However,when collecting and using it,the authority of the data subject may be violated.The data often identifies its subject by itself,and even if it is not a personal information that infringes on an individual’s authority,the moment it is connected,it becomes important and sensitive personal information that we have never thought of.Therefore,recent privacy regulations such as GDPR(GeneralData ProtectionRegulation)are changing to guarantee more rights of the data subjects.To use data effectively without infringing on the rights of the data subject,the concept of de-identification has been created.Researchers and companies can make personal information less identifiable through appropriate de-identification/pseudonymization and use the data for the purpose of statistical research.De-identification/pseudonymization techniques have been studied a lot,but it is difficult for companies and researchers to know how to de-identify/pseudonymize data.It is difficult to clearly understand how and to what extent each organization should take deidentification measures.Currently,each organization does not systematically analyze and conduct the situation but only takes minimal action while looking at the guidelines distributed by each country.We solved this problem from the perspective of risk management.Several steps are required to secure the dataset starting from pre-processing to releasing the dataset.We can analyze the dataset,analyze the risk,evaluate the risk,and treat the risk appropriately.The outcomes of each step can then be used to take appropriate action on the dataset to eliminate or reduce its risk.Then,we can release the dataset under its own purpose.These series of processes were reconstructed to fit the current situation by analyzing various standards such as ISO/IEC(International Organization for Standardization/International Electrotechnical Commission)20889,NIST IR(National Institute of Standards and Technology Interagency Reports)8053,NIST SP(National Institute of Standards and Technology Special Publications)800-188,and ITU-T(International Telecommunications Union-Telecommunication)X.1148.We propose an integrated framework based on situational awareness model and risk management model.We found that this framework can be specialized for multiple domains,and it is useful because it is based on a variety of case and utility-based ROI calculations.展开更多
Face de-identification has become increasingly important as the image sources are explosively growing and easily accessible.The advance of new face recognition techniques also arises people's concern regarding the...Face de-identification has become increasingly important as the image sources are explosively growing and easily accessible.The advance of new face recognition techniques also arises people's concern regarding the privacy leakage. The mainstream pipelines of face de-identification are mostly based on the k-same framework,which bears critiques of low effectiveness and poor visual quality.In this paper,we propose a new framework called Privacy-Protective-GAN (PP-GAN) that adapts GAN (generative adversarial network)with novel verificator and regulator modules specially designed for the face de-identification problem to ensure generating de-identified output with retained structure similarity according to a single input.We evaluate the proposed approach in terms of privacy protection,utility preservation,and structure similarity.Our approach not only outperforms existing face de-identification techniques but also provides a practical framework of adapting GAN with priors of domain knowledge.展开更多
With the advancement of video recording devices and network infrastructure,we use surveillance cameras to protect our valuable assets.This paper proposes a novel system for encrypting personal information within recor...With the advancement of video recording devices and network infrastructure,we use surveillance cameras to protect our valuable assets.This paper proposes a novel system for encrypting personal information within recorded surveillance videos to enhance efficiency and security.The proposed method leverages Dlib’s CNN-based facial recognition technology to identify Regions of Interest(ROIs)within the video,linking these ROIs to generate unique IDs.These IDs are then combined with a master key to create entity-specific keys,which are used to encrypt the ROIs within the video.This system supports selective decryption,effectively protecting personal information using surveillance footage.Additionally,the system overcomes the limitations of existing ROI recognition technologies by predicting unrecognized frames through post-processing.This research validates the proposed technology through experimental evaluations of execution time and post-processing techniques,ensuring comprehensive personal information protection.Guidelines for setting the thresholds used in this process are also provided.Implementing the proposed method could serve as an effective solution to security vulnerabilities that traditional approaches fail to address.展开更多
With the development of artificial intelligence(AI)technologies,biomedical imaging data play an important role in scientific research and clinical application,but the available resources are limited.Here we present Op...With the development of artificial intelligence(AI)technologies,biomedical imaging data play an important role in scientific research and clinical application,but the available resources are limited.Here we present Open Biomedical Imaging Archive(OBIA),a repository for archiving biomedical imaging and related clinical data.OBIA adopts five data objects(Collection,Individual,Study,Series,and Image)for data organization,and accepts the submission of biomedical images of multiple modalities,organs,and diseases.In order to protect personal privacy,OBIA has formulated a unified de-identification and quality control process.In addition,OBIA provides friendly and intuitive web interfaces for data submission,browsing,and retrieval,as well as image retrieval.As of September 2023,OBIA has housed data for a total of 937 individuals,4136 studies,24,701 series,and 1,938,309 images covering 9 modalities and 30 anatomical sites.Collectively,OBIA provides a reliable platform for biomedical imaging data management and offers free open access to all publicly available data to support research activities throughout the world.OBIA can be accessed at https://ngdc.cncb.ac.cn/obia.展开更多
基金CAMS Innovation Fund for Medical Sciences(CIFMS):“Construction of an Intelligent Management and Efficient Utilization Technology System for Big Data in Population Health Science.”(2021-I2M-1-057)Key Projects of the Innovation Fund of the National Clinical Research Center for Orthopedics and Sports Rehabilitation:“National Orthopedics and Sports Rehabilitation Real-World Research Platform System Construction”(23-NCRC-CXJJ-ZD4)。
文摘【Objective】Medical imaging data has great value,but it contains a significant amount of sensitive information about patients.At present,laws and regulations regarding to the de-identification of medical imaging data are not clearly defined around the world.This study aims to develop a tool that meets compliance-driven desensitization requirements tailored to diverse research needs.【Methods】To enhance the security of medical image data,we designed and implemented a DICOM format medical image de-identification system on the Windows operating system.【Results】Our custom de-identification system is adaptable to the legal standards of different countries and can accommodate specific research demands.The system offers both web-based online and desktop offline de-identification capabilities,enabling customization of de-identification rules and facilitating batch processing to improve efficiency.【Conclusions】This medical image de-identification system robustly strengthens the stewardship of sensitive medical data,aligning with data security protection requirements while facilitating the sharing and utilization of medical image data.This approach unlocks the intrinsic value inherent in such datasets.
文摘As technology develops,the amount of information being used has increased a lot.Every company learns big data to provide customized services with its customers.Accordingly,collecting and analyzing data of the data subject has become one of the core competencies of the companies.However,when collecting and using it,the authority of the data subject may be violated.The data often identifies its subject by itself,and even if it is not a personal information that infringes on an individual’s authority,the moment it is connected,it becomes important and sensitive personal information that we have never thought of.Therefore,recent privacy regulations such as GDPR(GeneralData ProtectionRegulation)are changing to guarantee more rights of the data subjects.To use data effectively without infringing on the rights of the data subject,the concept of de-identification has been created.Researchers and companies can make personal information less identifiable through appropriate de-identification/pseudonymization and use the data for the purpose of statistical research.De-identification/pseudonymization techniques have been studied a lot,but it is difficult for companies and researchers to know how to de-identify/pseudonymize data.It is difficult to clearly understand how and to what extent each organization should take deidentification measures.Currently,each organization does not systematically analyze and conduct the situation but only takes minimal action while looking at the guidelines distributed by each country.We solved this problem from the perspective of risk management.Several steps are required to secure the dataset starting from pre-processing to releasing the dataset.We can analyze the dataset,analyze the risk,evaluate the risk,and treat the risk appropriately.The outcomes of each step can then be used to take appropriate action on the dataset to eliminate or reduce its risk.Then,we can release the dataset under its own purpose.These series of processes were reconstructed to fit the current situation by analyzing various standards such as ISO/IEC(International Organization for Standardization/International Electrotechnical Commission)20889,NIST IR(National Institute of Standards and Technology Interagency Reports)8053,NIST SP(National Institute of Standards and Technology Special Publications)800-188,and ITU-T(International Telecommunications Union-Telecommunication)X.1148.We propose an integrated framework based on situational awareness model and risk management model.We found that this framework can be specialized for multiple domains,and it is useful because it is based on a variety of case and utility-based ROI calculations.
基金the National Natural Science Foundation of China under Grant No.61528204the National Science Foundation of USA under Grant No.1350521.
文摘Face de-identification has become increasingly important as the image sources are explosively growing and easily accessible.The advance of new face recognition techniques also arises people's concern regarding the privacy leakage. The mainstream pipelines of face de-identification are mostly based on the k-same framework,which bears critiques of low effectiveness and poor visual quality.In this paper,we propose a new framework called Privacy-Protective-GAN (PP-GAN) that adapts GAN (generative adversarial network)with novel verificator and regulator modules specially designed for the face de-identification problem to ensure generating de-identified output with retained structure similarity according to a single input.We evaluate the proposed approach in terms of privacy protection,utility preservation,and structure similarity.Our approach not only outperforms existing face de-identification techniques but also provides a practical framework of adapting GAN with priors of domain knowledge.
基金supported by the Institute of Information and Communications Technology Planning and Evaluation (IITP)funded by the Korea Government (MIST),Development of Collection and Integrated Analysis Methods of Automotive Inter and Intra System Artifacts through Construction of Event-Based Experimental System,under RS-2022-II221022.
文摘With the advancement of video recording devices and network infrastructure,we use surveillance cameras to protect our valuable assets.This paper proposes a novel system for encrypting personal information within recorded surveillance videos to enhance efficiency and security.The proposed method leverages Dlib’s CNN-based facial recognition technology to identify Regions of Interest(ROIs)within the video,linking these ROIs to generate unique IDs.These IDs are then combined with a master key to create entity-specific keys,which are used to encrypt the ROIs within the video.This system supports selective decryption,effectively protecting personal information using surveillance footage.Additionally,the system overcomes the limitations of existing ROI recognition technologies by predicting unrecognized frames through post-processing.This research validates the proposed technology through experimental evaluations of execution time and post-processing techniques,ensuring comprehensive personal information protection.Guidelines for setting the thresholds used in this process are also provided.Implementing the proposed method could serve as an effective solution to security vulnerabilities that traditional approaches fail to address.
基金supported by grants from the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB38050300)the Genomics Data Center Operation and Maintenance of Chinese Academy of Sciences(Grant No.CAS-WX2022SDC-XK05)the Key Technology Talent Program of the Chinese Academy of Sciences,China.
文摘With the development of artificial intelligence(AI)technologies,biomedical imaging data play an important role in scientific research and clinical application,but the available resources are limited.Here we present Open Biomedical Imaging Archive(OBIA),a repository for archiving biomedical imaging and related clinical data.OBIA adopts five data objects(Collection,Individual,Study,Series,and Image)for data organization,and accepts the submission of biomedical images of multiple modalities,organs,and diseases.In order to protect personal privacy,OBIA has formulated a unified de-identification and quality control process.In addition,OBIA provides friendly and intuitive web interfaces for data submission,browsing,and retrieval,as well as image retrieval.As of September 2023,OBIA has housed data for a total of 937 individuals,4136 studies,24,701 series,and 1,938,309 images covering 9 modalities and 30 anatomical sites.Collectively,OBIA provides a reliable platform for biomedical imaging data management and offers free open access to all publicly available data to support research activities throughout the world.OBIA can be accessed at https://ngdc.cncb.ac.cn/obia.
