In the last few years, deep-learning models are becoming crucial for numerous scientific and industrial applications. Due to the growth and complexity of deep neural networks, researchers have been investigating techn...
详细信息
Private Set Intersection (PSI) is one of the most important functions in secure multiparty computation (MPC). PSI protocols have been a practical cryptographic primitive and there are many privacy-preserving applicati...
Private Set Intersection (PSI) is one of the most important functions in secure multiparty computation (MPC). PSI protocols have been a practical cryptographic primitive and there are many privacy-preserving applications based on PSI protocols such as computing conversion of advertising and distributed computation. Private Set Intersection Cardinality (PSI-CA) is a useful variant of PSI protocol. PSI and PSI-CA allow several parties, each holding a private set, to jointly compute the intersection and cardinality, respectively without leaking any additional information. Nowadays, most PSI protocols mainly focus on two-party settings, while in multiparty settings, parties are able to share more valuable information and thus more desirable. On the other hand, with the advent of cloud computing, delegating computation to an untrusted server becomes an interesting problem. However, most existing delegated PSI protocols are unable to efficiently scale to multiple clients. In order to solve these problems, this paper proposes MDPPC, an efficient PSI protocol which supports scalable multiparty delegated PSI and PSI-CA operations. Security analysis shows that MDPPC is secure against semi-honest adversaries and it allows any number of colluding clients. For 15 parties with set size of 2 20 on server side and 2 16 on clients side, MDPPC costs only 81 seconds in PSI and 80 seconds in PSI-CA, respectively. The experimental results show that MDPPC has high scalability.
Rapidly generated data and the amount magnitude of data analytical jobs pose great pressure to the underlying computing facilities. A distributed multi-cluster computing environment such as a hybrid cloud consequently...
详细信息
ISBN:
(纸本)9781450388160
Rapidly generated data and the amount magnitude of data analytical jobs pose great pressure to the underlying computing facilities. A distributed multi-cluster computing environment such as a hybrid cloud consequently raises its necessity due to its advantages in adapting geographically distributed and potentially cloud-based computing resources. Different clusters forming such an environment could be heterogeneous and may be resource-elastic as well. From analytical perspective, in accordance with increasing needs on streaming applications and timely analytical demands, many data analytical jobs nowadays are time-critical in terms of their temporal urgency. And the overall workload of the computing environment can be hybrid to contain both time-critical and general applications. These all call for an efficient resource management approach capable to apprehend both computing environment and application features. However, the added up complexity and high dynamics of the system greatly hinder the performance of traditional rule-based approaches. In this work, we propose to utilize deep reinforcement learning for developing elasticity-compatible resource management for a heterogeneous distributedcomputing environment, aiming for less occurrences of missing temporal deadline while maintaining low average execution time ratio. Along with reinforcement learning we design a deep model employing Long Short-Term Memory (LSTM) structure and partial model sharing for multi-target learning mechanism. The experimental results show that the proposed approach could greatly outperform baselines and serve as a robust resource management for variant workloads.
Communication networks have been extensively deployed as an important infrastructure of power grid. To ensure the robustness of power communication networks, the fault prediction mechanism plays a pivotal role for the...
详细信息
With the development of the economy and the industrial structure regulation, the load characteristics of users and industries are affected by a growing number of factors. The accuracy of load forecasting methods that ...
详细信息
The Internet of Things (IoT) plays a significant role in shaping different aspects of our lives. IoT devices have become increasingly important due to their ability to connect, collect, and analyze data, automate proc...
The Internet of Things (IoT) plays a significant role in shaping different aspects of our lives. IoT devices have become increasingly important due to their ability to connect, collect, and analyze data, automate processes, improve safety and efficiency, and deliver personalized experiences. However, the advancement in quantum computer development poses a significant threat to resource-constrained IoT devices. This new generation of computers can break the classic public-key cryptographic schemes and digital signatures implemented in these IoT devices. While protecting IoT devices from quantum computer attacks poses many challenges, researchers are continuously making significant progress in developing lightweight post-quantum cryptographic algorithms for efficient key exchange mechanisms and digital signature algorithms tailored to IoT devices to overcome this issue. This paper proposes Q-SECURE, a post-Quantum resistant Security Enhancing Cryptography for Unified Resource-constrained device Encryption. A novel scheme that enables any IoT system to leverage the assistance of other devices in the network to gain the capability to generate any proposed post-quantum cryptographic key of a given size using distributed and parallelcomputing.
Artificial neural networks are widely used in various fields, such as intelligent road networks, Internet of Things, and smart medical systems due to their ability to process large amounts of data in parallel, store i...
Artificial neural networks are widely used in various fields, such as intelligent road networks, Internet of Things, and smart medical systems due to their ability to process large amounts of data in parallel, store information in a distributed manner, and self-organize and self-learn. Cloud computing technology has further expanded the development of neural network applications. However, user data often contains sensitive information, and once the data management right is transferred to the cloud, it faces serious security and privacy issues. In the medical field, privacy-preserving implementation of classification algorithms is crucial for ensuring the privacy of electronic medical diagnosis services. Current privacy-preserving medical pre-diagnosis schemes based on homomorphic encryption impose a significant computational and communication burden on users and servers. This paper proposes an efficient privacy-preserving medical pre-diagnosis scheme based on neural networks and inner product function encryption that protects user privacy during pre-diagnosis while having small computational and communication overheads.
In recommender systems, due to the lack of explicit feedback features, datasets with implicit feedback are always accustomed to train all samples without separating them during model training, without considering the ...
详细信息
China ADS front-end demo linac (CAFe) uses a distributed control system based on experimental physics and industrial control system (EPICS), and the EPICS software system Alarms is used to monitor the alarm states of ...
China ADS front-end demo linac (CAFe) uses a distributed control system based on experimental physics and industrial control system (EPICS), and the EPICS software system Alarms is used to monitor the alarm states of process variable(PV) in real time. Alarms stores a large amount of alarm data of time series representing alarm events, and the cause of failure can be determined by analyzing the correlation between alarm events. The traditional association rule algorithm is limited by the minimum support and can only get the association rules among frequent alarm events. Therefore, this paper proposes a parallel association rules algorithm, called CApriori, based on Spark, the big data computing engine, for processing the large amount of time series alarm data to find the association rules between low-support alarm events. In the second stage of the CApriori, distance correlation is introduced to remove candidate sets that of high frequency but low correlation. The proposed algorithm is applied to the data generated by the CAFe alarm system, and the results show that CApriori can find the association rules between the alarm events with high correlation and low support, which provides a basis for the intelligent fault diagnosis of the accelerator.
The proceedings contain 8 papers. The topics discussed include: molecular-continuum flow simulation in the exascale and big data era;an efficient halo approach for Euler-Lagrange simulations based on MPI-3 shared memo...
ISBN:
(纸本)9781450383035
The proceedings contain 8 papers. The topics discussed include: molecular-continuum flow simulation in the exascale and big data era;an efficient halo approach for Euler-Lagrange simulations based on MPI-3 shared memory;advantages of space-time finite elements for domains with time varying topology;node-level performance optimizations in CFD codes;multi-scale modelling of urban air pollution with coupled weather forecast and traffic simulation on HPC architecture;single-precision calculation of iterative refinement of eigenpairs of a real symmetric-definite generalized eigenproblem by using a filter composed of a single resolvent;high performance simulations of quantum transport using manycore computing;distributed MLPerf ResNet50 training on intel xeon architectures with TensorFlow;and efficient parallel multigrid method on Intel Xeon Phi clusters.
暂无评论