Although the existing legal norms and judicial practic-es can provide basic guidance for the right to personal data portabili-ty, it can be concluded that there are obstacles to the realization of this right through e...Although the existing legal norms and judicial practic-es can provide basic guidance for the right to personal data portabili-ty, it can be concluded that there are obstacles to the realization of this right through empirical research of the privacy policies of 66 mobile apps, such as whether they have stipulations on the right to personal data portability, whether they are able to derive copies of personal in-formation automatically, whether there are textual examples, whether ID verification is required, whether the copied documents are encrypt-ed, and whether the scope of personal information involved is consis-tent. This gap in practice, on the one hand, reflects the misunderstand-ing of the right to personal data portability, and on the other hand, is a result of the negative externalities, practical costs and technical lim-itations of the right to personal data portability. Based on rethinking the right to data portability, we can somehow solve practical problems concerning the right to personal data portability through multiple measures such as promoting the fulfillment of this right by legislation, optimizing technology-oriented operations, refining response process mechanisms, and enhancing system interoperability.展开更多
Personal credit risk assessment is an important part of the development of financial enterprises. Big data credit investigation is an inevitable trend of personal credit risk assessment, but some data are missing and ...Personal credit risk assessment is an important part of the development of financial enterprises. Big data credit investigation is an inevitable trend of personal credit risk assessment, but some data are missing and the amount of data is small, so it is difficult to train. At the same time, for different financial platforms, we need to use different models to train according to the characteristics of the current samples, which is time-consuming. <span style="font-family:Verdana;">In view of</span><span style="font-family:Verdana;"> these two problems, this paper uses the idea of transfer learning to build a transferable personal credit risk model based on Instance-based Transfer Learning (Instance-based TL). The model balances the weight of the samples in the source domain, and migrates the existing large dataset samples to the target domain of small samples, and finds out the commonness between them. At the same time, we have done a lot of experiments on the selection of base learners, including traditional machine learning algorithms and ensemble learning algorithms, such as decision tree, logistic regression, </span><span style="font-family:Verdana;">xgboost</span> <span style="font-family:Verdana;">and</span><span style="font-family:Verdana;"> so on. The datasets are from P2P platform and bank, the results show that the AUC value of Instance-based TL is 24% higher than that of the traditional machine learning model, which fully proves that the model in this paper has good application value. The model’s evaluation uses AUC, prediction, recall, F1. These criteria prove that this model has good application value from many aspects. At present, we are trying to apply this model to more fields to improve the robustness and applicability of the model;on the other hand, we are trying to do more in-depth research on domain adaptation to enrich the model.</span>展开更多
In the context of today's big data and cloud computing,the global flow of data has become a powerful driver for international economic and investment growth.The EU and the U.S.have created two different paths for ...In the context of today's big data and cloud computing,the global flow of data has become a powerful driver for international economic and investment growth.The EU and the U.S.have created two different paths for the legal regulation of the cross-border flow of personal data due to their respective historical traditions and realistic demands.The requirements for data protection have shown significant differences.The EU advocates localization of data and firmly restricts cross-border flow of personal data.The U.S.tends to protect personal data through industry self-regulation and government law enforcement.At the same time,these two paths also merge and supplement with each other.Based on this,China needs to learn from the legal regulatory paths of the EU and the US,respectively,to establish a legal idea that places equal emphasis on personal data protection and the development of the information industry.In terms of domestic law,the Cybersecurity Law of the People's Republic of China needs to be improved and supplemented by relevant supporting legislation to improve the operability of the law;the industry self-discipline guidelines should be established;and various types of cross-border data need to be classified and supervised.In terms of international law,it is necessary to participate in international cooperation based on the priority of data sovereignty and promote the signing of bilateral,multilateral agreements,and international treaties on the cross-border flow of personal data.展开更多
Cross-border data flows not only involve cross-border trade issues,but also severely challenge personal information protection,national data security,and the jurisdiction of justice and enforcement.As the current digi...Cross-border data flows not only involve cross-border trade issues,but also severely challenge personal information protection,national data security,and the jurisdiction of justice and enforcement.As the current digital trade negotiations could not accommodate these challenges,China has initiated the concept of secure cross-border data flow and has launched a dual-track multi-level regulatory system,including control system for overseas transfer of important data,system of crossborder provision of personal information,and system of cross-border data request for justice and enforcement.To explore a global regulatory framework for cross-border data flows,legitimate and controllable cross-border data flows should be promoted,supervision should be categorized based on risk concerned,and the rule of law should be coordinated at home and abroad to promote system compatibility.To this end,the key is to build a compatible regulatory framework,which includes clarifying the scope of important data to define the“Negative List”for preventing national security risks,improving the cross-border accountability for protecting personal information rights and interests to ease pre-supervision pressure,and focusing on data access rights instead of data localization for upholding the jurisdiction of justice and enforcement.展开更多
基金the current result of the “research on the basic category system of contemporary Chinese digital law” (23&ZD154), a major project of the National Social Science Fund of China.
文摘Although the existing legal norms and judicial practic-es can provide basic guidance for the right to personal data portabili-ty, it can be concluded that there are obstacles to the realization of this right through empirical research of the privacy policies of 66 mobile apps, such as whether they have stipulations on the right to personal data portability, whether they are able to derive copies of personal in-formation automatically, whether there are textual examples, whether ID verification is required, whether the copied documents are encrypt-ed, and whether the scope of personal information involved is consis-tent. This gap in practice, on the one hand, reflects the misunderstand-ing of the right to personal data portability, and on the other hand, is a result of the negative externalities, practical costs and technical lim-itations of the right to personal data portability. Based on rethinking the right to data portability, we can somehow solve practical problems concerning the right to personal data portability through multiple measures such as promoting the fulfillment of this right by legislation, optimizing technology-oriented operations, refining response process mechanisms, and enhancing system interoperability.
文摘Personal credit risk assessment is an important part of the development of financial enterprises. Big data credit investigation is an inevitable trend of personal credit risk assessment, but some data are missing and the amount of data is small, so it is difficult to train. At the same time, for different financial platforms, we need to use different models to train according to the characteristics of the current samples, which is time-consuming. <span style="font-family:Verdana;">In view of</span><span style="font-family:Verdana;"> these two problems, this paper uses the idea of transfer learning to build a transferable personal credit risk model based on Instance-based Transfer Learning (Instance-based TL). The model balances the weight of the samples in the source domain, and migrates the existing large dataset samples to the target domain of small samples, and finds out the commonness between them. At the same time, we have done a lot of experiments on the selection of base learners, including traditional machine learning algorithms and ensemble learning algorithms, such as decision tree, logistic regression, </span><span style="font-family:Verdana;">xgboost</span> <span style="font-family:Verdana;">and</span><span style="font-family:Verdana;"> so on. The datasets are from P2P platform and bank, the results show that the AUC value of Instance-based TL is 24% higher than that of the traditional machine learning model, which fully proves that the model in this paper has good application value. The model’s evaluation uses AUC, prediction, recall, F1. These criteria prove that this model has good application value from many aspects. At present, we are trying to apply this model to more fields to improve the robustness and applicability of the model;on the other hand, we are trying to do more in-depth research on domain adaptation to enrich the model.</span>
基金This article is supported by Law and Technology Institute,Renmin University of China.All mistakes and omissions are the responsibility of the author.
文摘In the context of today's big data and cloud computing,the global flow of data has become a powerful driver for international economic and investment growth.The EU and the U.S.have created two different paths for the legal regulation of the cross-border flow of personal data due to their respective historical traditions and realistic demands.The requirements for data protection have shown significant differences.The EU advocates localization of data and firmly restricts cross-border flow of personal data.The U.S.tends to protect personal data through industry self-regulation and government law enforcement.At the same time,these two paths also merge and supplement with each other.Based on this,China needs to learn from the legal regulatory paths of the EU and the US,respectively,to establish a legal idea that places equal emphasis on personal data protection and the development of the information industry.In terms of domestic law,the Cybersecurity Law of the People's Republic of China needs to be improved and supplemented by relevant supporting legislation to improve the operability of the law;the industry self-discipline guidelines should be established;and various types of cross-border data need to be classified and supervised.In terms of international law,it is necessary to participate in international cooperation based on the priority of data sovereignty and promote the signing of bilateral,multilateral agreements,and international treaties on the cross-border flow of personal data.
基金This article is funded by National Social Science Foundation’s general project“Theoretical and Practical Research on International Criminal Judicial Assistance in Combating Cybercrime”(Project No.:19BFX073)National Social Science Foundation’s major project“Translation,Research and Database Construction of Cyberspace Policies and Regulations”(Project No.:20&ZD179).
文摘Cross-border data flows not only involve cross-border trade issues,but also severely challenge personal information protection,national data security,and the jurisdiction of justice and enforcement.As the current digital trade negotiations could not accommodate these challenges,China has initiated the concept of secure cross-border data flow and has launched a dual-track multi-level regulatory system,including control system for overseas transfer of important data,system of crossborder provision of personal information,and system of cross-border data request for justice and enforcement.To explore a global regulatory framework for cross-border data flows,legitimate and controllable cross-border data flows should be promoted,supervision should be categorized based on risk concerned,and the rule of law should be coordinated at home and abroad to promote system compatibility.To this end,the key is to build a compatible regulatory framework,which includes clarifying the scope of important data to define the“Negative List”for preventing national security risks,improving the cross-border accountability for protecting personal information rights and interests to ease pre-supervision pressure,and focusing on data access rights instead of data localization for upholding the jurisdiction of justice and enforcement.
