The popularization of intelligent healthcare devices and big data analytics significantly boosts the development of Smart Healthcare Networks(SHNs).To enhance the precision of diagnosis,different participants in SHNs ...The popularization of intelligent healthcare devices and big data analytics significantly boosts the development of Smart Healthcare Networks(SHNs).To enhance the precision of diagnosis,different participants in SHNs share health data that contain sensitive information.Therefore,the data exchange process raises privacy concerns,especially when the integration of health data from multiple sources(linkage attack)results in further leakage.Linkage attack is a type of dominant attack in the privacy domain,which can leverage various data sources for private data mining.Furthermore,adversaries launch poisoning attacks to falsify the health data,which leads to misdiagnosing or even physical damage.To protect private health data,we propose a personalized differential privacy model based on the trust levels among users.The trust is evaluated by a defined community density,while the corresponding privacy protection level is mapped to controllable randomized noise constrained by differential privacy.To avoid linkage attacks in personalized differential privacy,we design a noise correlation decoupling mechanism using a Markov stochastic process.In addition,we build the community model on a blockchain,which can mitigate the risk of poisoning attacks during differentially private data transmission over SHNs.Extensive experiments and analysis on real-world datasets have testified the proposed model,and achieved better performance compared with existing research from perspectives of privacy protection and effectiveness.展开更多
In trying to explain why Hong Kong of China ranks highest in life expectancy in the world,we review what various experts are hypothesizing,and how data science methods may be used to provide more evidence-based conclu...In trying to explain why Hong Kong of China ranks highest in life expectancy in the world,we review what various experts are hypothesizing,and how data science methods may be used to provide more evidence-based conclusions.While more data become available,we find some data analysis studies were too simplistic,while others too overwhelming in answering this challenging question.We find the approach that analyzes life expectancy related data(mortality causes and rate for different cohorts)inspiring,and use this approach to study a carefully selected set of targets for comparison.In discussing the factors that matter,we argue that it is more reasonable to try to identify a set of factors that together explain the phenomenon.展开更多
One method often used in long-range weather forecasting is to analyse a series of historical data. In the early stage of the development of this method, an intuitive graphical method was used. Later a stationary Autor...One method often used in long-range weather forecasting is to analyse a series of historical data. In the early stage of the development of this method, an intuitive graphical method was used. Later a stationary Autoregressive Model (AR) was adopted to make a quantitative prediction statistically. But the AR model has some difficulty in dealing with data with seasonal variation. Therefore, data of the same month of展开更多
基金supported by the National Key Research and Development Program of China(No.2021YFF0900400).
文摘The popularization of intelligent healthcare devices and big data analytics significantly boosts the development of Smart Healthcare Networks(SHNs).To enhance the precision of diagnosis,different participants in SHNs share health data that contain sensitive information.Therefore,the data exchange process raises privacy concerns,especially when the integration of health data from multiple sources(linkage attack)results in further leakage.Linkage attack is a type of dominant attack in the privacy domain,which can leverage various data sources for private data mining.Furthermore,adversaries launch poisoning attacks to falsify the health data,which leads to misdiagnosing or even physical damage.To protect private health data,we propose a personalized differential privacy model based on the trust levels among users.The trust is evaluated by a defined community density,while the corresponding privacy protection level is mapped to controllable randomized noise constrained by differential privacy.To avoid linkage attacks in personalized differential privacy,we design a noise correlation decoupling mechanism using a Markov stochastic process.In addition,we build the community model on a blockchain,which can mitigate the risk of poisoning attacks during differentially private data transmission over SHNs.Extensive experiments and analysis on real-world datasets have testified the proposed model,and achieved better performance compared with existing research from perspectives of privacy protection and effectiveness.
基金support of funding(No.UGC/IDS(R)11/21)from the Hong Kong SAR Government.
文摘In trying to explain why Hong Kong of China ranks highest in life expectancy in the world,we review what various experts are hypothesizing,and how data science methods may be used to provide more evidence-based conclusions.While more data become available,we find some data analysis studies were too simplistic,while others too overwhelming in answering this challenging question.We find the approach that analyzes life expectancy related data(mortality causes and rate for different cohorts)inspiring,and use this approach to study a carefully selected set of targets for comparison.In discussing the factors that matter,we argue that it is more reasonable to try to identify a set of factors that together explain the phenomenon.
文摘One method often used in long-range weather forecasting is to analyse a series of historical data. In the early stage of the development of this method, an intuitive graphical method was used. Later a stationary Autoregressive Model (AR) was adopted to make a quantitative prediction statistically. But the AR model has some difficulty in dealing with data with seasonal variation. Therefore, data of the same month of
