Anomaly detection has practical significance for finding unusual patterns in time series.However,most existing algorithms may lose some important information in time series presentation and have high time complexity.A...Anomaly detection has practical significance for finding unusual patterns in time series.However,most existing algorithms may lose some important information in time series presentation and have high time complexity.Another problem is that privacy-preserving was not taken into account in these algorithms.In this paper,we propose a new data structure named Interval Hash Table(IHTable)to capture more original information of time series and design a fast anomaly detection algorithm based on Interval Hash Table(ADIHT).The key insight of ADIHT is distributions of normal subsequences are always similar while distributions of anomaly subsequences are different and random by contrast.Furthermore,to make our proposed algorithm fit for anomaly detection under multiple participation,we propose a privacy-preserving anomaly detection scheme named OP-ADIHT based on ADIHT and homomorphic encryption.Compared with existing anomaly detection schemes with privacy-preserving,OP-ADIHT needs less communication cost and calculation cost.Security analysis of different circumstances also shows that OP-ADIHT will not leak the privacy information of participants.Extensive experiments results show that ADIHT can outperform most anomaly detection algorithms and perform close to the best results in terms of AUC-ROC,and ADIHT needs the least time.展开更多
A systematic fuzzy approach is developed to model fuzziness and uncertainties in the preferences of decision makers involved in a conflict. This unique fuzzy preference formulation is used within the paradigm of the G...A systematic fuzzy approach is developed to model fuzziness and uncertainties in the preferences of decision makers involved in a conflict. This unique fuzzy preference formulation is used within the paradigm of the Graph Model for Conflict Resolution in which a given dispute is modeled in terms of decision makers, each decision maker＇s courses of actions or options, and each decision maker＇s preferences concerning the states or outcomes which could take place. In order to be able to determine the stability of each state for each decision maker and the possible equilibria or resolutions, a range of solution concepts describing potential human behavior under conflict are defined for use with fuzzy preferences. More specifically, strong and weak definitions of stability are provided for the solution concepts called Nash, general metarational, symmetric metarational, and sequential stability. To illustrate how these solution concepts can be conveniently used in practice, they are applied to a dispute over the contamination of an aquifer by a chemical company located in Elmira, Ontario, Canada.展开更多
基金supported by Natural Science Foundation of Guangdong Province,China(Grant No.2020A1515010970)Shenzhen Research Council(Grant No.JCYJ20200109113427092,GJHZ20180928155209705).
文摘Anomaly detection has practical significance for finding unusual patterns in time series.However,most existing algorithms may lose some important information in time series presentation and have high time complexity.Another problem is that privacy-preserving was not taken into account in these algorithms.In this paper,we propose a new data structure named Interval Hash Table(IHTable)to capture more original information of time series and design a fast anomaly detection algorithm based on Interval Hash Table(ADIHT).The key insight of ADIHT is distributions of normal subsequences are always similar while distributions of anomaly subsequences are different and random by contrast.Furthermore,to make our proposed algorithm fit for anomaly detection under multiple participation,we propose a privacy-preserving anomaly detection scheme named OP-ADIHT based on ADIHT and homomorphic encryption.Compared with existing anomaly detection schemes with privacy-preserving,OP-ADIHT needs less communication cost and calculation cost.Security analysis of different circumstances also shows that OP-ADIHT will not leak the privacy information of participants.Extensive experiments results show that ADIHT can outperform most anomaly detection algorithms and perform close to the best results in terms of AUC-ROC,and ADIHT needs the least time.
基金funded by a Discovery Grant from the Natural Science and Engineering Research Council(NSERC)of Canada as well as a grant from the Government of Saudi Arabia.
文摘A systematic fuzzy approach is developed to model fuzziness and uncertainties in the preferences of decision makers involved in a conflict. This unique fuzzy preference formulation is used within the paradigm of the Graph Model for Conflict Resolution in which a given dispute is modeled in terms of decision makers, each decision maker＇s courses of actions or options, and each decision maker＇s preferences concerning the states or outcomes which could take place. In order to be able to determine the stability of each state for each decision maker and the possible equilibria or resolutions, a range of solution concepts describing potential human behavior under conflict are defined for use with fuzzy preferences. More specifically, strong and weak definitions of stability are provided for the solution concepts called Nash, general metarational, symmetric metarational, and sequential stability. To illustrate how these solution concepts can be conveniently used in practice, they are applied to a dispute over the contamination of an aquifer by a chemical company located in Elmira, Ontario, Canada.
