Frequent cyber-attacks compel service providers to employ security-aware service functions (S-SFs) while delivering network services. Typically, one S-SF can be implemented by diverse configurations, each requiring di...
详细信息
Frequent cyber-attacks compel service providers to employ security-aware service functions (S-SFs) while delivering network services. Typically, one S-SF can be implemented by diverse configurations, each requiring different implementation costs and providing various security levels. These multi-configured S-SFs could compose various security-aware service function chains (S-SFCs) to satisfy the security requirement of incoming network request. How to properly compose an S-SFC and effectively deploy it remains an open challenging problem. In this work, we discover the "reDundancy security A ccumulatio N "(DAN) phenomenon caused by the direct-summation-fashion when calculating the security level (SeL) of an S-SFC and propose novel methodology to estimate the SeL of one S-SFC for avoiding DAN. To begin, we introduce the concept security level indicator (SeLI) and our novel methodology. Next, we formulate the problem of security-aware SF selection, chaining, and deployment (Sec-SFCD) with the objective function of cost optimization and prove its NP-hardness. To solve this problem, we propose the security-cost-balance (SCB) factor technique, which measures the average cost of satisfying one unit of security requirement. Based on this technique, we further develop an efficient algorithm called SCB-based S-SFC deployment (SCB-SD) and improves it by proposing overflowing security level elimination (OSE) technique. Through our thorough analysis, we show the logarithm approximation of SCB-SD and SCB-SD with OSE technique (SSD-OSE). The extensive simulation results validate SSD-OSE' s logarithm-approximation and demonstrate that it significantly outperforms the benchmarks directly extended from the state-of-the-art by an average of 17.98 % and 67.47 %.
Recently, the in-network computing (INC) technique has been widely adopted by various applications including the reliability-sensitive ones such as remote surgery, and autonomous vehicle systems. To deploy reliable IN...
详细信息
Recently, the in-network computing (INC) technique has been widely adopted by various applications including the reliability-sensitive ones such as remote surgery, and autonomous vehicle systems. To deploy reliable INCenabled services, redundant task replicas are hosted by network devices to meet a specified service reliability threshold such as 99.9%, 99.99%, and 99.999%. Most existing works assume that this threshold is solely impacted by the software reliability while neglecting the hardware reliability. This neglectedness likely leads to unexpected service interruptions when the software replicas are co-deployed over one unreliable hardware. This work jointly considers the heterogeneous reliability brought by both software and hardware and identifies a novel phenomenon called "Software-Reliability-Only Experience Degradation"(SRO-ED). To address this, we mathematically establish the INC-enabled services adoption with heterogeneous reliability (ISAHR) problem to optimize service costs and prove its NP-hardness. We introduce an effective Cost-Reliability (CR) measure to indicate the average cost needed to satisfy each reliability unit while considering both software and hardware reliabilities. Next, we propose an innovative algorithm called CR measure-based INC services deployment (CRD), which is proved to be logarithm-approximate in cost optimization. Extensive simulation results validate the logarithmic approximation of CR-D, and show that it outperforms the benchmarks by an average of 29.42% and 35.77% in cost optimization.
Multicast greatly benefits many emerging applications such as federated learning, metaverse, and data warehouse. Recently, due to frequent cyber-attacks, multicast services have tended to request rigorous security agr...
详细信息
Multicast greatly benefits many emerging applications such as federated learning, metaverse, and data warehouse. Recently, due to frequent cyber-attacks, multicast services have tended to request rigorous security agreements, which likely differ among the destinations. To meet such agreements, one can employ security-aware service functions (SFs) to construct the security-aware SF tree (S-SFT) for multicast services. A security-aware SF can be provided by various vendors with diverse configurations and implementation costs. The multi-configured SFs and the various security agreements will add significant complexity to the deployment process of the security-aware multicast request. In this work, for the first time, we study how to effectively compose and embed an S-SFT over the network with multiple vendors. We formulate the problem of security- aware SFT composing and embedding. We develop a new technique called cost-security-centrality (CSC) based on the pigeonhole' s principle and propose a heuristic algorithm called CSC-based S-SFT deployment (CSC-SD). Via thorough mathematical proofs, we show that CSC-SD is logarithm approximate. Extensive simulations show that CSC-SD significantly outperforms the benchmarks and reveal that more function sharing facilitates saving implementation cost, but more routing sharing does not indicate saving routing cost.
People's opinions are often affected by their social network, and the associated misinformation on the online social networks can easily mislead people's judgment and decision-making process, leading people to...
详细信息
People's opinions are often affected by their social network, and the associated misinformation on the online social networks can easily mislead people's judgment and decision-making process, leading people to take unconventional or even radical behaviors. People's decision-making behavior is influenced by their concern to the misinformation they receive. Building on this, we explore the competitive concern minimization problem of leveraging agents who post correct information to minimize users' concern to misinformation. First, considering users' concern to misinformation, this paper constructs a concern-critical competitive model and introduces the Coulomb's law to quantify the dynamic evolution of users' concern in information diffusion. Second, we prove hardness results for the competitive concern minimization problem and discuss the modularity of the objective function. Then, to optimize the nonsubmodular objective function, a two-stage approximate projected subgradient algorithm with data-dependent approximation ratio is developed using Lovasz extension and convex envelope. Finally, the experimental simulations on three real networks highlight the efficiency of the approaches proposed in this paper, which is at least 9.71% better than other baselines in reducing misinformation concern.
Data privacy has become one of the most important concerns in the big data era. Because of its broad applications in machine learning and data analysis, many algorithms and theoretical results have been established fo...
详细信息
Data privacy has become one of the most important concerns in the big data era. Because of its broad applications in machine learning and data analysis, many algorithms and theoretical results have been established for privacy clustering problems, such as k-means and k-median problems with privacy protection. However, there is little work on privacy protection in k-center clustering. Our research focuses on the k-center problem, its distributed variant, and the distributed k-center problem under differential privacy constraints. These problems model the concept of safeguarding the privacy of individual input elements, with the integration of differential privacy aimed at ensuring the security of individual information during data processing and analysis. We propose three approximation algorithms for these problems, respectively, and achieve a constant factor approximation ratio.
We have designed a novel polynomial-time approximate algorithm for the graph vertex colouring problem. Contrary to the common top-down strategy for solving the colouring graph problem, we propose a bottom-up algorithm...
详细信息
Li transient concentration distribution in spherical active material particles can affect the maximum power density and the safe operating regime of the electric vehicles(EVs). On one hand, the quasiexact/exact soluti...
详细信息
Li transient concentration distribution in spherical active material particles can affect the maximum power density and the safe operating regime of the electric vehicles(EVs). On one hand, the quasiexact/exact solution obtained in the time/frequency domain is time-consuming and just as a reference value for approximate solutions;on the other hand, calculation errors and application range of approximate solutions not only rely on approximate algorithms but also on discharge modes. For the purpose to track the transient dynamics for Li solid-phase diffusion in spherical active particles with a tolerable error range and for a wide applicable range, it is necessary to choose optimal approximate algorithms in terms of discharge modes and the nature of active material particles. In this study, approximation methods,such as diffusion length method, polynomial profile approximation method, Padé approximation method,pseudo steady state method, eigenfunction-based Galerkin collocation method, and separation of variables method for solving Li solid-phase diffusion in spherical active particles are compared from calculation fundamentals to algorithm implementation. Furthermore, these approximate solutions are quantitatively compared to the quasi-exact/exact solution in the time/frequency domain under typical discharge modes, i.e., start-up, slow-down, and speed-up. The results obtained from the viewpoint of time-frequency analysis offer a theoretical foundation on how to track Li transient concentration profile in spherical active particles with a high precision and for a wide application range. In turn, optimal solutions of Li solid diffusion equations for spherical active particles can improve the reliability in predicting safe operating regime and estimating maximum power for automotive batteries.
Federated learning, a new distributed learning paradigm, has the advantage of sharing model information without revealing data privacy. However, considering the selfishness of organizations, they will not participate ...
详细信息
ISBN:
(数字)9789819708086
ISBN:
(纸本)9789819708079;9789819708086
Federated learning, a new distributed learning paradigm, has the advantage of sharing model information without revealing data privacy. However, considering the selfishness of organizations, they will not participate in federated learning without compensation. To address this problem, in this paper, we design a feature importance-aware vertical federated learning incentive mechanism. We first synthesize a small amount of data locally using the interpolation method at the organization and send it to the coordinator for evaluating the contribution of each feature to the learning task. Then, the coordinator calculates the importance value of each feature in the dataset for the current task using the Shapley value method according to the synthetic data. Next, we formulate the process of organization participation in the federation as a feature importance maximization problem based on reverse auction which is a knapsack auction problem. Finally, we design an approximate algorithm to solve the proposed optimization problem and the solution of the approximation algorithm is shown to be 1/2 -approximate to the optimal solution. Furthermore, we prove that the proposed mechanism is truthfulness, individual rationality, and computational efficiency. The superiority of our proposed mechanism is verified through experiments on real-world datasets.
Sketch algorithms are crucial for identifying top-k items in largescale data streams. Existing methods often compromise between performance and accuracy, unable to efficiently handle increasing data volumes with limit...
详细信息
ISBN:
(纸本)9798400704369
Sketch algorithms are crucial for identifying top-k items in largescale data streams. Existing methods often compromise between performance and accuracy, unable to efficiently handle increasing data volumes with limited memory. We present Bubble Sketch, a compact algorithm that excels in both performance and accuracy. Bubble Sketch achieves this by (1) Recording only full keys of hot items, significantly reducing memory usage, and (2) Using threshold relocation to resolve conflicts, enhancing detection accuracy. Unlike traditional methods, Bubble Sketch eliminates the need for a MinHeap, ensuring fast processing speeds. Experiments show Bubble Sketch outperforms the other seven algorithms compared, with the highest throughput and precision, and surpasses HeavyKeeper in accuracy by up to two orders of magnitude.
Link quality is important and can greatly affect the performance of wireless transmission algorithms and protocols. Currently, researchers have proposed a variety of approaches to implement link quality estimation. Ho...
详细信息
Link quality is important and can greatly affect the performance of wireless transmission algorithms and protocols. Currently, researchers have proposed a variety of approaches to implement link quality estimation. However, the estimated result of link quality is not accurate enough and the error is large, so they may lead to the failure of routing algorithm and protocol. In this paper, a novel method is proposed to achieve the more accurate estimation of link quality than before. This method employs Bernoulli sampling-based algorithm to complete the estimation of link quality. The problem is modeled as calculation of estimators based on Bernoulli sampling data. The authors further prove that the calculation results are accurate by probability theory. Furthermore, according to link quality estimation, the authors also provide a centralized routing algorithm and a distributed improvement algorithm in order to switch the data transmission on the better quality link. Finally, the extensive experiment results indicate that the proposed methods obtain high performance in terms of energy consumption and accuracy.
暂无评论