In this paper, we consider the problems of data sharing between multiple distrusted authorities. Prior solutions rely on trusted third parties such as CAs, or are susceptible to collusion between malicious authorities...In this paper, we consider the problems of data sharing between multiple distrusted authorities. Prior solutions rely on trusted third parties such as CAs, or are susceptible to collusion between malicious authorities, which can comprise the security of honest ones. In this paper, we propose a new multi-authority data sharing scheme - Decen- tralized Multi-Authority ABE （DMA）, which is derived from CP-ABE that is resilient to these types of misbehavior. Our system distin- guishes between a data owner （DO） principal and attribute authorities （AAs）： the DO owns the data but allows AAs to arbitrate access by providing attribute labels to users. The data is protected by policy encryption over these attributes. Unlike prior systems, attributes generated by AAs are not user-specific, and neither is the system susceptible to collusion between users who try to escalate their access by sharing keys. We prove our scherne correct under the Decisional Bilinear Diffie-Hellman （DBDH） assumption; we also include a com- plete end-to-end implementation that demon- strates the practical efficacy of our technique.展开更多
Offloading Mobile Devices(MDs)computation tasks to Edge Nodes(ENs)is a promising solution to overcome computation and energy resources limitations of MDs.However,there exists an unreasonable profit allocation problem ...Offloading Mobile Devices(MDs)computation tasks to Edge Nodes(ENs)is a promising solution to overcome computation and energy resources limitations of MDs.However,there exists an unreasonable profit allocation problem between MDs and ENs caused by the excessive concern on MD profit.In this paper,we propose an auction-based computation offloading algorithm,inspiring ENs to provide high-quality service by maximizing the profit of ENs.Firstly,a novel cooperation auction framework is designed to avoid overall profit damage of ENs,which is derived from the high computation delay at the overloaded ENs.Thereafter,the bidding willingness of each MD in every round of auction is determined to ensure MD rationality.Furthermore,we put forward a payment rule for the pre-selected winner to effectively guarantee auction truthfulness.Finally,the auction-based profit maximization offloading algorithm is proposed,and the MD is allowed to occupy the computation and spectrum resources of the EN for offloading if it wins the auction.Numerical results verify the performance of the proposed algorithm.Compared with the VA algorithm,the ENs profit is increased by 23.8%,and the task discard ratio is decreased by 7.5%.展开更多
Objective:To investigate the in vitro anti-HIV-1 activities and its associated mechanism of action of an extract isolated from Phyllanthus urinaria (P.urinaria) and to develop an HPLC test method for detecting gallic ...Objective:To investigate the in vitro anti-HIV-1 activities and its associated mechanism of action of an extract isolated from Phyllanthus urinaria (P.urinaria) and to develop an HPLC test method for detecting gallic acid (GA) in plasma and tissues to study its pharmacokinetics and tissue distribution in rats.Methods:An extract of P.urinaria was isolated and purified by phytochemistry and chromatography techniques.The anti-HIV-1 activities and toxicities of the extract and its component GA were determined in human T lymph cells (MT-4) by theMTTr method.The mechanism of its anti-HIV-1 action was studied to examine the in vitro binding of its components with HIV-1 target proteins by Biacore technique.The pharmacokinetics and tissue distribution of GA were investigated after oral administration of polyphenol extract (PE) and pure GA in rats.The concentrations of GA in plasma and tissues were determined by HPLC.Results:The PE and GA isolated from P.urinaria had anti-HIV-1 activities with IC50s of 0.61 μg/mL and 0.76 μg/mL,respectively.The Biacore study indicated that PE and GA interacted with HIV-1 RT,gp120,and P24.The pharmacokinetic parameters Tmax,Cm ax,AUC0-t,and T1/2 for GA were (60.0 ± 3.0) minutes,(2.87 ± 0.50) μg·mL-1,(343.5 ± 11.2) mg·min·L-1,and (113.3 ± 9.3) minutes while the parameters for GA in the PE were (10.0 ± 1.3) minutes,(3.89 ± 0.90) μg·mL-1,(394.7 ± 14.0) mg· min· L-1,and (81.7 ± 4.1) minutes,respectively.GA was detected in rat lungs,liver,kidneys,heart and spleen.Conclusion:APE isolated from P.urinaria containing GA has anti-HIV-1 activities.GA is quickly absorbed and slowly eliminated in rats after oral administration.The pharmacokinetics of GA administered as a PE is desirable,and it is widely distributed in the main tissues of lung and liver.Both its properties and anti-HIV-1 activities make it of interest for further studies.展开更多
Vehicular Edge Computing(VEC)enhances the quality of user services by deploying wealth of resources near vehicles.However,due to highly dynamic and complex nature of vehicular networks,centralized decisionmaking for r...Vehicular Edge Computing(VEC)enhances the quality of user services by deploying wealth of resources near vehicles.However,due to highly dynamic and complex nature of vehicular networks,centralized decisionmaking for resource allocation proves inadequate within VECs.Conversely,allocating resources via distributed decision-making consumes vehicular resources.To improve the quality of user service,we formulate a problem of latency minimization,further subdividing this problem into two subproblems to be solved through distributed decision-making.To mitigate the resource consumption caused by distributed decision-making,we propose Reinforcement Learning(RL)algorithm based on sequential alternating multi-agent system mechanism,which effectively reduces the dimensionality of action space without losing the informational content of action,achieving network lightweighting.We discuss the rationality,generalizability,and inherent advantages of proposed mechanism.Simulation results indicate that our proposed mechanism outperforms traditional RL algorithms in terms of stability,generalizability,and adaptability to scenarios with invalid actions,all while achieving network lightweighting.展开更多
基金supported by the National Natural Science Foundation of China under grant 61402160Hunan Provincial Natural Science Foundation of China under grant 2016JJ3043Open Funding for Universities in Hunan Province under grant 14K023
文摘In this paper, we consider the problems of data sharing between multiple distrusted authorities. Prior solutions rely on trusted third parties such as CAs, or are susceptible to collusion between malicious authorities, which can comprise the security of honest ones. In this paper, we propose a new multi-authority data sharing scheme - Decen- tralized Multi-Authority ABE （DMA）, which is derived from CP-ABE that is resilient to these types of misbehavior. Our system distin- guishes between a data owner （DO） principal and attribute authorities （AAs）： the DO owns the data but allows AAs to arbitrate access by providing attribute labels to users. The data is protected by policy encryption over these attributes. Unlike prior systems, attributes generated by AAs are not user-specific, and neither is the system susceptible to collusion between users who try to escalate their access by sharing keys. We prove our scherne correct under the Decisional Bilinear Diffie-Hellman （DBDH） assumption; we also include a com- plete end-to-end implementation that demon- strates the practical efficacy of our technique.
基金supported by National Natural Science Foundation of China under grants 61901070,61801065,61771082,61871062,U20A20157in part by the Science and Technology Research Program of Chongqing Municipal Education Commission under grants KJQN202000603,KJQN201900611+1 种基金in part by the Natural Science Foundation of Chongqing under grant cstc2020jcyjzdxmX0024part by University Innovation Research Group of Chongqing under grant CXQT20017.
文摘Offloading Mobile Devices(MDs)computation tasks to Edge Nodes(ENs)is a promising solution to overcome computation and energy resources limitations of MDs.However,there exists an unreasonable profit allocation problem between MDs and ENs caused by the excessive concern on MD profit.In this paper,we propose an auction-based computation offloading algorithm,inspiring ENs to provide high-quality service by maximizing the profit of ENs.Firstly,a novel cooperation auction framework is designed to avoid overall profit damage of ENs,which is derived from the high computation delay at the overloaded ENs.Thereafter,the bidding willingness of each MD in every round of auction is determined to ensure MD rationality.Furthermore,we put forward a payment rule for the pre-selected winner to effectively guarantee auction truthfulness.Finally,the auction-based profit maximization offloading algorithm is proposed,and the MD is allowed to occupy the computation and spectrum resources of the EN for offloading if it wins the auction.Numerical results verify the performance of the proposed algorithm.Compared with the VA algorithm,the ENs profit is increased by 23.8%,and the task discard ratio is decreased by 7.5%.
基金The experiment was supported by the National Natural Science Foundation of China(30171197)Natural Science Foundation of Beijing(7073093).
文摘Objective:To investigate the in vitro anti-HIV-1 activities and its associated mechanism of action of an extract isolated from Phyllanthus urinaria (P.urinaria) and to develop an HPLC test method for detecting gallic acid (GA) in plasma and tissues to study its pharmacokinetics and tissue distribution in rats.Methods:An extract of P.urinaria was isolated and purified by phytochemistry and chromatography techniques.The anti-HIV-1 activities and toxicities of the extract and its component GA were determined in human T lymph cells (MT-4) by theMTTr method.The mechanism of its anti-HIV-1 action was studied to examine the in vitro binding of its components with HIV-1 target proteins by Biacore technique.The pharmacokinetics and tissue distribution of GA were investigated after oral administration of polyphenol extract (PE) and pure GA in rats.The concentrations of GA in plasma and tissues were determined by HPLC.Results:The PE and GA isolated from P.urinaria had anti-HIV-1 activities with IC50s of 0.61 μg/mL and 0.76 μg/mL,respectively.The Biacore study indicated that PE and GA interacted with HIV-1 RT,gp120,and P24.The pharmacokinetic parameters Tmax,Cm ax,AUC0-t,and T1/2 for GA were (60.0 ± 3.0) minutes,(2.87 ± 0.50) μg·mL-1,(343.5 ± 11.2) mg·min·L-1,and (113.3 ± 9.3) minutes while the parameters for GA in the PE were (10.0 ± 1.3) minutes,(3.89 ± 0.90) μg·mL-1,(394.7 ± 14.0) mg· min· L-1,and (81.7 ± 4.1) minutes,respectively.GA was detected in rat lungs,liver,kidneys,heart and spleen.Conclusion:APE isolated from P.urinaria containing GA has anti-HIV-1 activities.GA is quickly absorbed and slowly eliminated in rats after oral administration.The pharmacokinetics of GA administered as a PE is desirable,and it is widely distributed in the main tissues of lung and liver.Both its properties and anti-HIV-1 activities make it of interest for further studies.
基金supported by the National Natural Science Foundation of China(62271096,U20A20157)Science and Technology Research Program of Chongqing Municipal Education Commission(KJQN202000626)+4 种基金Natural Science Foundation of Chongqing,China(cstc2020jcyjzdxmX0024)University Innovation Research Group of Chongqing(CXQT20017)Youth Innovation Group Support Program of ICE Discipline of CQUPT(SCIE-QN-2022-04)Chongqing Postdoctoral Science Special Foundation(2021XM3058)Chongqing Postgraduate Research and Innovation Project under grant(CYB22250).
文摘Vehicular Edge Computing(VEC)enhances the quality of user services by deploying wealth of resources near vehicles.However,due to highly dynamic and complex nature of vehicular networks,centralized decisionmaking for resource allocation proves inadequate within VECs.Conversely,allocating resources via distributed decision-making consumes vehicular resources.To improve the quality of user service,we formulate a problem of latency minimization,further subdividing this problem into two subproblems to be solved through distributed decision-making.To mitigate the resource consumption caused by distributed decision-making,we propose Reinforcement Learning(RL)algorithm based on sequential alternating multi-agent system mechanism,which effectively reduces the dimensionality of action space without losing the informational content of action,achieving network lightweighting.We discuss the rationality,generalizability,and inherent advantages of proposed mechanism.Simulation results indicate that our proposed mechanism outperforms traditional RL algorithms in terms of stability,generalizability,and adaptability to scenarios with invalid actions,all while achieving network lightweighting.
