The structure of key-value data is a typical data structure generated by mobile devices.The collection and analysis of the data from mobile devices are critical for service providers to improve service quality.Neverth...The structure of key-value data is a typical data structure generated by mobile devices.The collection and analysis of the data from mobile devices are critical for service providers to improve service quality.Nevertheless,collecting raw data,which may contain various per⁃sonal information,would lead to serious personal privacy leaks.Local differential privacy(LDP)has been proposed to protect privacy on the device side so that the server cannot obtain the raw data.However,existing mechanisms assume that all keys are equally sensitive,which can⁃not produce high-precision statistical results.A utility-improved data collection framework with LDP for key-value formed mobile data is pro⁃posed to solve this issue.More specifically,we divide the key-value data into sensitive and non-sensitive parts and only provide an LDPequivalent privacy guarantee for sensitive keys and all values.We instantiate our framework by using a utility-improved key value-unary en⁃coding(UKV-UE)mechanism based on unary encoding,with which our framework can work effectively for a large key domain.We then vali⁃date our mechanism which provides better utility and is suitable for mobile devices by evaluating it in two real datasets.Finally,some pos⁃sible future research directions are envisioned.展开更多
Machine learning advancements in healthcare have made data collected through smartphones and wearable devices a vital source of public health and medical insights.While wearable device data help to monitor,detect,and ...Machine learning advancements in healthcare have made data collected through smartphones and wearable devices a vital source of public health and medical insights.While wearable device data help to monitor,detect,and predict diseases and health conditions,some data owners hesitate to share such sensitive data with companies or researchers due to privacy concerns.Moreover,wearable devices have been recently available as commercial products;thus large,diverse,and representative datasets are not available to most researchers.In this article,the authors propose an open marketplace where wearable device users securely monetize their wearable device records by sharing data with consumers(e.g.,researchers)to make wearable device data more available to healthcare researchers.To secure the data transactions in a privacy-preserving manner,the authors use a decentralized approach using Blockchain and Non-Fungible Tokens(NFTs).To ensure data originality and integrity with secure validation,the marketplace uses Trusted Execution Environments(TEE)in wearable devices to verify the correctness of health data.The marketplace also allows researchers to train models using Federated Learning with a TEE-backed secure aggregation of data users may not be willing to share.To ensure user participation,we model incentive mechanisms for the Federated Learning-based and anonymized data-sharing approaches using NFTs.The authors also propose using payment channels and batching to reduce smart contact gas fees and optimize user profits.If widely adopted,it’s believed that TEE and Blockchain-based incentives will promote the ethical use of machine learning with validated wearable device data in healthcare and improve user participation due to incentives.展开更多
This paper presents a substructure online hybrid test system that is extensible for geographically distributed tests. This system consists of a set of devices conventionally used for cyclic tests to load the tested su...This paper presents a substructure online hybrid test system that is extensible for geographically distributed tests. This system consists of a set of devices conventionally used for cyclic tests to load the tested substructures onto the target displacement or the target force. Due to their robustness and portability, individual sets of conventional loading devices can be transported and reconfigured to realize physical loading in geographically remote laboratories. Another appealing feature is the flexible displacement-force mixed control that is particularly suitable for specimens having large disparities in stiffness during various performance stages. To conduct a substructure online hybrid test, an extensible framework is developed, which is equipped with a generalized interface to encapsulate each substructure. Multiple tested substructures and analyzed substructures using various structural program codes can be accommodated within the single framework, simply interfaced with the boundary displacements and forces. A coordinator program is developed to keep the boundaries among all substructures compatible and equilibrated. An Interuet-based data exchange scheme is also devised to transfer data among computers equipped with different software environments. A series of online hybrid tests are introduced, and the portability, flexibility, and extensibility of the online hybrid test system are demonstrated.展开更多
According to the demand of substation secondary device dynamic performance testing, a smart substation field testing technique based on recurrence principle is proposed in the paper, and the characteristics of smart s...According to the demand of substation secondary device dynamic performance testing, a smart substation field testing technique based on recurrence principle is proposed in the paper, and the characteristics of smart substation secondary device digitization and information sharing are used by the technique. The principle of testing technique is as follow: the digital simulation model is constructed on the basis of the substation’s actual construction, then the simulating data highly similar to substation’s actual electric quantity transient process is generated, at last, the substation digital secondary device can be tested by using data “recurrence” technique. The testing technique is verified and applied by constructing testing system, the application results show that the technique can effectively perform field test on the dynamic performance of digital secondary device, and the technique has good engineering implementation and application value.展开更多
文摘The structure of key-value data is a typical data structure generated by mobile devices.The collection and analysis of the data from mobile devices are critical for service providers to improve service quality.Nevertheless,collecting raw data,which may contain various per⁃sonal information,would lead to serious personal privacy leaks.Local differential privacy(LDP)has been proposed to protect privacy on the device side so that the server cannot obtain the raw data.However,existing mechanisms assume that all keys are equally sensitive,which can⁃not produce high-precision statistical results.A utility-improved data collection framework with LDP for key-value formed mobile data is pro⁃posed to solve this issue.More specifically,we divide the key-value data into sensitive and non-sensitive parts and only provide an LDPequivalent privacy guarantee for sensitive keys and all values.We instantiate our framework by using a utility-improved key value-unary en⁃coding(UKV-UE)mechanism based on unary encoding,with which our framework can work effectively for a large key domain.We then vali⁃date our mechanism which provides better utility and is suitable for mobile devices by evaluating it in two real datasets.Finally,some pos⁃sible future research directions are envisioned.
文摘Machine learning advancements in healthcare have made data collected through smartphones and wearable devices a vital source of public health and medical insights.While wearable device data help to monitor,detect,and predict diseases and health conditions,some data owners hesitate to share such sensitive data with companies or researchers due to privacy concerns.Moreover,wearable devices have been recently available as commercial products;thus large,diverse,and representative datasets are not available to most researchers.In this article,the authors propose an open marketplace where wearable device users securely monetize their wearable device records by sharing data with consumers(e.g.,researchers)to make wearable device data more available to healthcare researchers.To secure the data transactions in a privacy-preserving manner,the authors use a decentralized approach using Blockchain and Non-Fungible Tokens(NFTs).To ensure data originality and integrity with secure validation,the marketplace uses Trusted Execution Environments(TEE)in wearable devices to verify the correctness of health data.The marketplace also allows researchers to train models using Federated Learning with a TEE-backed secure aggregation of data users may not be willing to share.To ensure user participation,we model incentive mechanisms for the Federated Learning-based and anonymized data-sharing approaches using NFTs.The authors also propose using payment channels and batching to reduce smart contact gas fees and optimize user profits.If widely adopted,it’s believed that TEE and Blockchain-based incentives will promote the ethical use of machine learning with validated wearable device data in healthcare and improve user participation due to incentives.
基金Public Benefit Research Foundation under Grant No.201108006Natural Science Foundation under Grant No.51161120360+2 种基金Heilongjiang Overseas Funding under Grant No.LC201002 of ChinaGrant-in-Aid for Scientific Research(Basic Research Category A,19206060)Japan Society for the Promotion of Science
文摘This paper presents a substructure online hybrid test system that is extensible for geographically distributed tests. This system consists of a set of devices conventionally used for cyclic tests to load the tested substructures onto the target displacement or the target force. Due to their robustness and portability, individual sets of conventional loading devices can be transported and reconfigured to realize physical loading in geographically remote laboratories. Another appealing feature is the flexible displacement-force mixed control that is particularly suitable for specimens having large disparities in stiffness during various performance stages. To conduct a substructure online hybrid test, an extensible framework is developed, which is equipped with a generalized interface to encapsulate each substructure. Multiple tested substructures and analyzed substructures using various structural program codes can be accommodated within the single framework, simply interfaced with the boundary displacements and forces. A coordinator program is developed to keep the boundaries among all substructures compatible and equilibrated. An Interuet-based data exchange scheme is also devised to transfer data among computers equipped with different software environments. A series of online hybrid tests are introduced, and the portability, flexibility, and extensibility of the online hybrid test system are demonstrated.
文摘According to the demand of substation secondary device dynamic performance testing, a smart substation field testing technique based on recurrence principle is proposed in the paper, and the characteristics of smart substation secondary device digitization and information sharing are used by the technique. The principle of testing technique is as follow: the digital simulation model is constructed on the basis of the substation’s actual construction, then the simulating data highly similar to substation’s actual electric quantity transient process is generated, at last, the substation digital secondary device can be tested by using data “recurrence” technique. The testing technique is verified and applied by constructing testing system, the application results show that the technique can effectively perform field test on the dynamic performance of digital secondary device, and the technique has good engineering implementation and application value.
