Nowadays,with the significant growth of the mobile market,security issues on the Android Operation System have also become an urgent matter.Trusted execution environment(TEE)technologies are considered an option for s...Nowadays,with the significant growth of the mobile market,security issues on the Android Operation System have also become an urgent matter.Trusted execution environment(TEE)technologies are considered an option for satisfying the inviolable property by taking advantage of hardware security.However,for Android,TEE technologies still contain restrictions and limitations.The first issue is that non-original equipment manufacturer developers have limited access to the functionality of hardware-based TEE.Another issue of hardware-based TEE is the cross-platform problem.Since every mobile device supports different TEE vendors,it becomes an obstacle for developers to migrate their trusted applications to other Android devices.A software-based TEE solution is a potential approach that allows developers to customize,package and deliver the product efficiently.Motivated by that idea,this paper introduces a VTEE model,a software-based TEE solution,on Android devices.This research contributes to the analysis of the feasibility of using a virtualized TEE on Android devices by considering two metrics:computing performance and security.The experiment shows that the VTEE model can host other software-based TEE services and deliver various cryptography TEE functions on theAndroid environment.The security evaluation shows that adding the VTEE model to the existing Android does not addmore security issues to the traditional design.Overall,this paper shows applicable solutions to adjust the balance between computing performance and security.展开更多
A centralized trusted execution environment(TEE)has been extensively studied to provide secure and trusted computing.However,a TEE might become a throughput bottleneck if it is used to evaluate data quality when colle...A centralized trusted execution environment(TEE)has been extensively studied to provide secure and trusted computing.However,a TEE might become a throughput bottleneck if it is used to evaluate data quality when collecting large-scale data in a crowdsourcing system.It may also have security problems compromised by attackers.Here,we propose a scheme,named dTEE,for building a platform for providing distributed trusted computing by leveraging TEEs.The platform is used as an infrastructure of trusted computations for blockchain-based crowdsourcing systems,especially to securely evaluate data quality and manage remuneration:these operations are handled by a TEE group.First,dTEE uses a public blockchain with smart contracts to manage TEEs without reliance on any trusted third parties.Second,to update TEE registration information and rule out zombie TEEs,dTEE uses a reporting mechanism.To attract TEE owners to join in and provide service of trusted computations,it uses a fair monetary incentive mechanism.Third,to account for malicious attackers,we design a model with Byzantine fault tolerance,not limited to a crash-failure model.Finally,we conduct an extensive evaluation of our design on a local cluster.The results show that dTEE finishes evaluating 10,000 images within one minute and achieves about 65 tps throughput when evaluating Sudoku solution data with collective signatures both in a group of 120 TEEs.展开更多
A reference point based multi-objective optimization using a combination between trust region (TR) algorithm and particle swarm optimization (PSO) to solve the multi-objective environmental/economic dispatch (EED) pro...A reference point based multi-objective optimization using a combination between trust region (TR) algorithm and particle swarm optimization (PSO) to solve the multi-objective environmental/economic dispatch (EED) problem is presented in this paper. The EED problem is handled by Reference Point Interactive Approach. One of the main advantages of the proposed approach is integrating the merits of both TR and PSO, where TR has provided the initial set (close to the Pareto set as possible and the reference point of the decision maker) followed by PSO to improve the quality of the solutions and get all the points on the Pareto frontier. The performance of the proposed algorithm is tested on standard IEEE 30-bus 6-genrator test system and is compared with conventional methods. The results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto-optimal non-dominated solutions in one single run. The comparison with the classical methods demonstrates the superiority of the proposed approach and confirms its potential to solve the multi-objective EED problem.展开更多
A trusted execution environment(TEE)is a system-on-chip and CPU system with a wide security solution available on today’s Arm application(APP)processors,which dominate the smartphone market.Generally,mobile APPs crea...A trusted execution environment(TEE)is a system-on-chip and CPU system with a wide security solution available on today’s Arm application(APP)processors,which dominate the smartphone market.Generally,mobile APPs create a trusted application(TA)in the TEE to process sensitive information,such as payment or message encryption,which is transparent to the APPs running in the rich execution environments(REEs).In detail,the REE and TEE interact and eventually send back the results to the APP in the REE through the interface provided by the TA.Such an operation definitely increases the overhead of mobile APPs.In this paper,we first present a comprehensive analysis of the performance of open-source TEE encrypted text.We then propose a high energy-efficient task scheduling strategy(ETS-TEE).By leveraging the deep learning algorithm,our policy considers the complexity of TA tasks,which are dynamically scheduled between modeling on the local device and offloading to an edge server.We evaluate our approach on Raspberry Pi 3B as the local mobile device and Jetson TX2 as the edge server.The results show that compared with the default scheduling strategy on the local device,our approach achieves an average of 38.0%energy reduction and 1.6×speedup.This greatly reduces the performance loss caused by mobile devices in order to protect the safe execution of applications,so that the trusted execution environment has both security and high performance.展开更多
Trusted Execution Environment(TEE)is an important part of the security architecture of modern mobile devices,but its secure interaction process brings extra computing burden to mobile devices.This paper takes open por...Trusted Execution Environment(TEE)is an important part of the security architecture of modern mobile devices,but its secure interaction process brings extra computing burden to mobile devices.This paper takes open portable trusted execution environment(OP-TEE)as the research object and deploys it to Raspberry Pi 3B,designs and implements a benchmark for OP-TEE,and analyzes its program characteristics.Furthermore,the application execution time,energy consumption and energy-delay product(EDP)are taken as the optimization objectives,and the central processing unit(CPU)frequency scheduling strategy of mobile devices is dynamically adjusted according to the characteristics of different applications through the combined model.The experimental result shows that compared with the default strategy,the scheduling method proposed in this paper saves 21.18%on average with the Line Regression-Decision Tree scheduling model with the shortest delay as the optimization objective.The Decision Tree-Support Vector Regression(SVR)scheduling model,which takes the lowest energy consumption as the optimization goal,saves 22%energy on average.The Decision Tree-K-Nearest Neighbor(KNN)scheduling model with the lowest EDP as the optimization objective optimizes about 33.9%on average.展开更多
基金This work was partly supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea Government(MSIT),(No.2020-0-00952,Development of 5G edge security technology for ensuring 5G+service stability and availability,50%)the Institute of Information and Communications Technology Planning and Evaluation(IITP)grant funded by the MSIT(Ministry of Science and ICT),Korea(No.IITP-2022-2020-0-01602,ITRC(Information Technology Research Center)support program,50%).
文摘Nowadays,with the significant growth of the mobile market,security issues on the Android Operation System have also become an urgent matter.Trusted execution environment(TEE)technologies are considered an option for satisfying the inviolable property by taking advantage of hardware security.However,for Android,TEE technologies still contain restrictions and limitations.The first issue is that non-original equipment manufacturer developers have limited access to the functionality of hardware-based TEE.Another issue of hardware-based TEE is the cross-platform problem.Since every mobile device supports different TEE vendors,it becomes an obstacle for developers to migrate their trusted applications to other Android devices.A software-based TEE solution is a potential approach that allows developers to customize,package and deliver the product efficiently.Motivated by that idea,this paper introduces a VTEE model,a software-based TEE solution,on Android devices.This research contributes to the analysis of the feasibility of using a virtualized TEE on Android devices by considering two metrics:computing performance and security.The experiment shows that the VTEE model can host other software-based TEE services and deliver various cryptography TEE functions on theAndroid environment.The security evaluation shows that adding the VTEE model to the existing Android does not addmore security issues to the traditional design.Overall,this paper shows applicable solutions to adjust the balance between computing performance and security.
基金This work was supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(No.NRF-2019R1A2C1090713).
文摘A centralized trusted execution environment(TEE)has been extensively studied to provide secure and trusted computing.However,a TEE might become a throughput bottleneck if it is used to evaluate data quality when collecting large-scale data in a crowdsourcing system.It may also have security problems compromised by attackers.Here,we propose a scheme,named dTEE,for building a platform for providing distributed trusted computing by leveraging TEEs.The platform is used as an infrastructure of trusted computations for blockchain-based crowdsourcing systems,especially to securely evaluate data quality and manage remuneration:these operations are handled by a TEE group.First,dTEE uses a public blockchain with smart contracts to manage TEEs without reliance on any trusted third parties.Second,to update TEE registration information and rule out zombie TEEs,dTEE uses a reporting mechanism.To attract TEE owners to join in and provide service of trusted computations,it uses a fair monetary incentive mechanism.Third,to account for malicious attackers,we design a model with Byzantine fault tolerance,not limited to a crash-failure model.Finally,we conduct an extensive evaluation of our design on a local cluster.The results show that dTEE finishes evaluating 10,000 images within one minute and achieves about 65 tps throughput when evaluating Sudoku solution data with collective signatures both in a group of 120 TEEs.
文摘A reference point based multi-objective optimization using a combination between trust region (TR) algorithm and particle swarm optimization (PSO) to solve the multi-objective environmental/economic dispatch (EED) problem is presented in this paper. The EED problem is handled by Reference Point Interactive Approach. One of the main advantages of the proposed approach is integrating the merits of both TR and PSO, where TR has provided the initial set (close to the Pareto set as possible and the reference point of the decision maker) followed by PSO to improve the quality of the solutions and get all the points on the Pareto frontier. The performance of the proposed algorithm is tested on standard IEEE 30-bus 6-genrator test system and is compared with conventional methods. The results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto-optimal non-dominated solutions in one single run. The comparison with the classical methods demonstrates the superiority of the proposed approach and confirms its potential to solve the multi-objective EED problem.
基金supported by the National Natural Science Foundation of China (No.61902229)Fundamental Research Funds for the Central Universities (No.GK202103084).
文摘A trusted execution environment(TEE)is a system-on-chip and CPU system with a wide security solution available on today’s Arm application(APP)processors,which dominate the smartphone market.Generally,mobile APPs create a trusted application(TA)in the TEE to process sensitive information,such as payment or message encryption,which is transparent to the APPs running in the rich execution environments(REEs).In detail,the REE and TEE interact and eventually send back the results to the APP in the REE through the interface provided by the TA.Such an operation definitely increases the overhead of mobile APPs.In this paper,we first present a comprehensive analysis of the performance of open-source TEE encrypted text.We then propose a high energy-efficient task scheduling strategy(ETS-TEE).By leveraging the deep learning algorithm,our policy considers the complexity of TA tasks,which are dynamically scheduled between modeling on the local device and offloading to an edge server.We evaluate our approach on Raspberry Pi 3B as the local mobile device and Jetson TX2 as the edge server.The results show that compared with the default scheduling strategy on the local device,our approach achieves an average of 38.0%energy reduction and 1.6×speedup.This greatly reduces the performance loss caused by mobile devices in order to protect the safe execution of applications,so that the trusted execution environment has both security and high performance.
基金funded by National Key Research and Development Program of China under Grant No.2019YFC1520904 from January 2020 to April 2023funded by Shaanxi Innovation Program under Grant 2023-CX-TD-04 January 2023 to December 2025.
文摘Trusted Execution Environment(TEE)is an important part of the security architecture of modern mobile devices,but its secure interaction process brings extra computing burden to mobile devices.This paper takes open portable trusted execution environment(OP-TEE)as the research object and deploys it to Raspberry Pi 3B,designs and implements a benchmark for OP-TEE,and analyzes its program characteristics.Furthermore,the application execution time,energy consumption and energy-delay product(EDP)are taken as the optimization objectives,and the central processing unit(CPU)frequency scheduling strategy of mobile devices is dynamically adjusted according to the characteristics of different applications through the combined model.The experimental result shows that compared with the default strategy,the scheduling method proposed in this paper saves 21.18%on average with the Line Regression-Decision Tree scheduling model with the shortest delay as the optimization objective.The Decision Tree-Support Vector Regression(SVR)scheduling model,which takes the lowest energy consumption as the optimization goal,saves 22%energy on average.The Decision Tree-K-Nearest Neighbor(KNN)scheduling model with the lowest EDP as the optimization objective optimizes about 33.9%on average.
