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2022-02-22
Olivier, Stephen L., Ellingwood, Nathan D., Berry, Jonathan, Dunlavy, Daniel M..  2021.  Performance Portability of an SpMV Kernel Across Scientific Computing and Data Science Applications. 2021 IEEE High Performance Extreme Computing Conference (HPEC). :1—8.
Both the data science and scientific computing communities are embracing GPU acceleration for their most demanding workloads. For scientific computing applications, the massive volume of code and diversity of hardware platforms at supercomputing centers has motivated a strong effort toward performance portability. This property of a program, denoting its ability to perform well on multiple architectures and varied datasets, is heavily dependent on the choice of parallel programming model and which features of the programming model are used. In this paper, we evaluate performance portability in the context of a data science workload in contrast to a scientific computing workload, evaluating the same sparse matrix kernel on both. Among our implementations of the kernel in different performance-portable programming models, we find that many struggle to consistently achieve performance improvements using the GPU compared to simple one-line OpenMP parallelization on high-end multicore CPUs. We show one that does, and its performance approaches and sometimes even matches that of vendor-provided GPU math libraries.
2022-02-09
Zheng, Shiyuan, Xie, Hong, Lui, John C.S..  2021.  Social Visibility Optimization in OSNs with Anonymity Guarantees: Modeling, Algorithms and Applications. 2021 IEEE 37th International Conference on Data Engineering (ICDE). :2063–2068.
Online social network (OSN) is an ideal venue to enhance one's visibility. This paper considers how a user (called requester) in an OSN selects a small number of available users and invites them as new friends/followers so as to maximize his "social visibility". More importantly, the requester has to do this under the anonymity setting, which means he is not allowed to know the neighborhood information of these available users in the OSN. In this paper, we first develop a mathematical model to quantify the social visibility and formulate the problem of visibility maximization with anonymity guarantee, abbreviated as "VisMAX-A". Then we design an algorithmic framework named as "AdaExp", which adaptively expands the requester's visibility in multiple rounds. In each round of the expansion, AdaExp uses a query oracle with anonymity guarantee to select only one available user. By using probabilistic data structures like the k-minimum values (KMV) sketch, we design an efficient query oracle with anonymity guarantees. We also conduct experiments on real-world social networks and validate the effectiveness of our algorithms.
Cinà, Antonio Emanuele, Vascon, Sebastiano, Demontis, Ambra, Biggio, Battista, Roli, Fabio, Pelillo, Marcello.  2021.  The Hammer and the Nut: Is Bilevel Optimization Really Needed to Poison Linear Classifiers? 2021 International Joint Conference on Neural Networks (IJCNN). :1–8.
One of the most concerning threats for modern AI systems is data poisoning, where the attacker injects maliciously crafted training data to corrupt the system's behavior at test time. Availability poisoning is a particularly worrisome subset of poisoning attacks where the attacker aims to cause a Denial-of-Service (DoS) attack. However, the state-of-the-art algorithms are computationally expensive because they try to solve a complex bi-level optimization problem (the ``hammer''). We observed that in particular conditions, namely, where the target model is linear (the ``nut''), the usage of computationally costly procedures can be avoided. We propose a counter-intuitive but efficient heuristic that allows contaminating the training set such that the target system's performance is highly compromised. We further suggest a re-parameterization trick to decrease the number of variables to be optimized. Finally, we demonstrate that, under the considered settings, our framework achieves comparable, or even better, performances in terms of the attacker's objective while being significantly more computationally efficient.
Zhao, Pengyuan, Yang, Shengqi, Chen, Zheng.  2021.  Relationship Anonymity Evaluation Model Based on Markov Chain. 2021 4th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE). :671–676.
In this paper, we propose a relational anonymous P2P communication network evaluation model based on Markov chain (AEMC), and show how to extend our model to the anonymous evaluation of sender and receiver relationship anonymity when the attacker attacks the anonymous P2P communication network and obtains some information. Firstly, the constraints of the evaluation model (the attacker assumption for message tracing) are specified in detail; then the construction of AEMC anonymous evaluation model and the specific evaluation process are described; finally, the simulation experiment is carried out, and the evaluation model is applied to the probabilistic anonymous evaluation of the sender and receiver relationship of the attacker model, and the evaluation is carried out from the perspective of user (message).
2022-02-08
Alsafwani, Nadher, Ali, Musab A. M., Tahir, Nooritawati Md.  2021.  Evaluation of the Mobile Ad Hoc Network (MANET) for Wormhole Attacks using Qualnet Simulator. 2021 IEEE 11th International Conference on System Engineering and Technology (ICSET). :46–49.
Security is the key concern, which allows safe communication between any two mobile nodes in an unfavorable environment. Wireless Ad Hoc can be unsecured against attacks by means of malicious nodes. Hence this study assesses the influence of wormhole attacks on Mobile Ad Hoc network (MANET) system that is evaluated and validated based on the QualNet simulator. The MANET performance is investigated utilizing the wormhole attacks. The simulation is performed on Mobile node's network layer and data link layer in the WANET (wireless Ad Hoc network). The MANET performance was examined using “what-if” analyses too. Results showed that for security purposes, it is indeed necessary to assess the Mobile Ad Hoc node deployment.
2022-02-07
Yang, Chen, Yang, Zepeng, Hou, Jia, Su, Yang.  2021.  A Lightweight Full Homomorphic Encryption Scheme on Fully-connected Layer for CNN Hardware Accelerator achieving Security Inference. 2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS). :1–4.
The inference results of neural network accelerators often involve personal privacy or business secrets in intelligent systems. It is important for the safety of convolutional neural network (CNN) accelerator to prevent the key data and inference result from being leaked. The latest CNN models have started to combine with fully homomorphic encryption (FHE), ensuring the data security. However, the computational complexity, data storage overhead, inference time are significantly increased compared with the traditional neural network models. This paper proposed a lightweight FHE scheme on fully-connected layer for CNN hardware accelerator to achieve security inference, which not only protects the privacy of inference results, but also avoids excessive hardware overhead and great performance degradation. Compared with state-of-the-art works, this work reduces computational complexity by approximately 90% and decreases ciphertext size by 87%∼95%.
Chkirbene, Zina, Hamila, Ridha, Erbad, Aiman, Kiranyaz, Serkan, Al-Emadi, Nasser, Hamdi, Mounir.  2021.  Cooperative Machine Learning Techniques for Cloud Intrusion Detection. 2021 International Wireless Communications and Mobile Computing (IWCMC). :837–842.
Cloud computing is attracting a lot of attention in the past few years. Although, even with its wide acceptance, cloud security is still one of the most essential concerns of cloud computing. Many systems have been proposed to protect the cloud from attacks using attack signatures. Most of them may seem effective and efficient; however, there are many drawbacks such as the attack detection performance and the system maintenance. Recently, learning-based methods for security applications have been proposed for cloud anomaly detection especially with the advents of machine learning techniques. However, most researchers do not consider the attack classification which is an important parameter for proposing an appropriate countermeasure for each attack type. In this paper, we propose a new firewall model called Secure Packet Classifier (SPC) for cloud anomalies detection and classification. The proposed model is constructed based on collaborative filtering using two machine learning algorithms to gain the advantages of both learning schemes. This strategy increases the learning performance and the system's accuracy. To generate our results, a publicly available dataset is used for training and testing the performance of the proposed SPC. Our results show that the accuracy of the SPC model increases the detection accuracy by 20% compared to the existing machine learning algorithms while keeping a high attack detection rate.
2022-02-04
Zhang, Mingyue.  2021.  System Component-Level Self-Adaptations for Security via Bayesian Games. 2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion). :102–104.

Security attacks present unique challenges to self-adaptive system design due to the adversarial nature of the environment. However, modeling the system as a single player, as done in prior works in security domain, is insufficient for the system under partial compromise and for the design of fine-grained defensive strategies where the rest of the system with autonomy can cooperate to mitigate the impact of attacks. To deal with such issues, we propose a new self-adaptive framework incorporating Bayesian game and model the defender (i.e., the system) at the granularity of components in system architecture. The system architecture model is translated into a Bayesian multi-player game, where each component is modeled as an independent player while security attacks are encoded as variant types for the components. The defensive strategy for the system is dynamically computed by solving the pure equilibrium to achieve the best possible system utility, improving the resiliency of the system against security attacks.

Belkaaloul, Abdallah, Bensaber, Boucif Amar.  2021.  Anonymous Authentication Protocol for Efficient Communications in Vehicle to Grid Networks. 2021 IEEE Symposium on Computers and Communications (ISCC). :1–5.
Rapid multiplication of electric vehicles requires the implementation of a new infrastructure to sustain their operations. For instance, charging these vehicles batteries necessitates a connection that allows information exchanges between vehicle and infrastructure. These exchanges are managed by a network called V2G (Vehicle to Grid), which is governed by the ISO 15118 standard. This last recommends the use of X.509 hierarchical PKI to protect the network communications against attacks. Although several authors have identified and criticized the shortcomings of this proposal, but no one provides a robust and effective remedial solution to alleviate them. This paper proposes an efficient protocol that addresses these shortcomings while respecting the concepts of the ISO 15118 standard. It fulfills the most important security requirements i.e. confidentiality, anonymity, integrity and non-repudiation. The validity and effectiveness of the proposed protocol were confirmed using the formal modeling tool Tamarin Prover and the RISE- V2G simulator.
2022-02-03
Xu, Chengtao, Song, Houbing.  2021.  Mixed Initiative Balance of Human-Swarm Teaming in Surveillance via Reinforcement learning. 2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC). :1—10.
Human-machine teaming (HMT) operates in a context defined by the mission. Varying from the complexity and disturbance in the cooperation between humans and machines, a single machine has difficulty handling work with humans in the scales of efficiency and workload. Swarm of machines provides a more feasible solution in such a mission. Human-swarm teaming (HST) extends the concept of HMT in the mission, such as persistent surveillance, search-and-rescue, warfare. Bringing the concept of HST faces several scientific challenges. For example, the strategies of allocation on the high-level decision making. Here, human usually plays the supervisory or decision making role. Performance of such fixed structure of HST in actual mission operation could be affected by the supervisor’s status from many aspects, which could be considered in three general parts: workload, situational awareness, and trust towards the robot swarm teammate and mission performance. Besides, the complexity of a single human operator in accessing multiple machine agents increases the work burdens. An interface between swarm teammates and human operators to simplify the interaction process is desired in the HST.In this paper, instead of purely considering the workload of human teammates, we propose the computational model of human swarm interaction (HSI) in the simulated map surveillance mission. UAV swarm and human supervisor are both assigned in searching a predefined area of interest (AOI). The workload allocation of map monitoring is adjusted based on the status of the human worker and swarm teammate. Workload, situation awareness ability, trust are formulated as independent models, which affect each other. A communication-aware UAV swarm persistent surveillance algorithm is assigned in the swarm autonomy portion. With the different surveillance task loads, the swarm agent’s thrust parameter adjusts the autonomy level to fit the human operator’s needs. Reinforcement learning is applied in seeking the relative balance of workload in both human and swarm sides. Metrics such as mission accomplishment rate, human supervisor performance, mission performance of UAV swarm are evaluated in the end. The simulation results show that the algorithm could learn the human-machine trust interaction to seek the workload balance to reach better mission execution performance. This work inspires us to leverage a more comprehensive HST model in more practical HMT application scenarios.
Lee, Hyo-Cheol, Lee, Seok-Won.  2021.  Towards Provenance-based Trust-aware Model for Socio-Technically Connected Self-Adaptive System. 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC). :761—767.
In a socio-technically connected environment, self-adaptive systems need to cooperate with others to collect information to provide context-dependent functionalities to users. A key component of ensuring safe and secure cooperation is finding trustworthy information and its providers. Trust is an emerging quality attribute that represents the level of belief in the cooperative environments and serves as a promising solution in this regard. In this research, we will focus on analyzing trust characteristics and defining trust-aware models through the trust-aware goal model and the provenance model. The trust-aware goal model is designed to represent the trust-related requirements and their relationships. The provenance model is analyzed as trust evidence to be used for the trust evaluation. The proposed approach contributes to build a comprehensive understanding of trust and design a trust-aware self-adaptive system. In order to show the feasibility of the proposed approach, we will conduct a case study with the crowd navigation system for an unmanned vehicle system.
2022-01-31
Alexopoulos, Ilias, Neophytou, Stelios, Kyriakides, Ioannis.  2021.  Identifying Metrics for an IoT Performance Estimation Framework. 2021 10th Mediterranean Conference on Embedded Computing (MECO). :1–6.
In this work we introduce a framework to support design decisions for heterogeneous IoT platforms and devices. The framework methodology as well as the development of software and hardware models are outlined. Specific factors that affect the performance of device are identified and formulated in a metric form. The performance aspects are embedded in a flexible and scalable framework for decision support. An indicative experimental setup investigates the applicability of the framework for a specific functional block. The experimental results are used to assess the significance of the framework under development.
2022-01-25
Marulli, Fiammetta, Balzanella, Antonio, Campanile, Lelio, Iacono, Mauro, Mastroianni, Michele.  2021.  Exploring a Federated Learning Approach to Enhance Authorship Attribution of Misleading Information from Heterogeneous Sources. 2021 International Joint Conference on Neural Networks (IJCNN). :1–8.
Authorship Attribution (AA) is currently applied in several applications, among which fraud detection and anti-plagiarism checks: this task can leverage stylometry and Natural Language Processing techniques. In this work, we explored some strategies to enhance the performance of an AA task for the automatic detection of false and misleading information (e.g., fake news). We set up a text classification model for AA based on stylometry exploiting recurrent deep neural networks and implemented two learning tasks trained on the same collection of fake and real news, comparing their performances: one is based on Federated Learning architecture, the other on a centralized architecture. The goal was to discriminate potential fake information from true ones when the fake news comes from heterogeneous sources, with different styles. Preliminary experiments show that a distributed approach significantly improves recall with respect to the centralized model. As expected, precision was lower in the distributed model. This aspect, coupled with the statistical heterogeneity of data, represents some open issues that will be further investigated in future work.
Wang, Mingyue, Miao, Yinbin, Guo, Yu, Wang, Cong, Huang, Hejiao, Jia, Xiaohua.  2021.  Attribute-based Encrypted Search for Multi-owner and Multi-user Model. ICC 2021 - IEEE International Conference on Communications. :1–7.
Nowadays, many data owners choose to outsource their data to public cloud servers while allowing authorized users to retrieve them. To protect data confidentiality from an untrusted cloud, many studies on searchable encryption (SE) are proposed for privacy-preserving search over encrypted data. However, most of the existing SE schemes only focus on the single-owner model. Users need to search one-by-one among data owners to retrieve relevant results even if data are from the same cloud server, which inevitably incurs unnecessary bandwidth and computation cost to users. Thus, how to enable efficient authorized search over multi-owner datasets remains to be fully explored. In this paper, we propose a new privacy-preserving search scheme for the multi-owner and multi-user model. Our proposed scheme has two main advantages: 1) We achieve an attribute-based keyword search for multi-owner model, where users can only search datasets from specific authorized owners. 2) Each data owner can enforce its own fine-grained access policy for users while an authorized user only needs to generate one trapdoor (i.e., encrypted search keyword) to search over multi-owner encrypted data. Through rigorous security analysis and performance evaluation, we demonstrate that our scheme is secure and feasible.
Contașel, Cristian, Trancă, Dumitru-Cristian, Pălăcean, Alexandru-Viorel.  2021.  Cloud based mobile application security enforcement using device attestation API. 2021 20th RoEduNet Conference: Networking in Education and Research (RoEduNet). :1–5.
Today the mobile devices are more and more present in our lives, and the mobile applications market has experienced a sharp growth. Most of these applications are made to make our daily lives easier, and for this a large part of them consume various web services. Given this transition, from desktop and web applications to mobile applications, many critical services have begun to expose their APIs for use by such application clients. Unfortunately, this transition has paved the way for new vulnerabilities, vulnerabilities used to compress cloud services. In this article we analyzed the main security problems and how they can be solved using the attestation services, the services that indicate that the device running the application and the client application are genuine.
2022-01-10
Freas, Christopher B., Shah, Dhara, Harrison, Robert W..  2021.  Accuracy and Generalization of Deep Learning Applied to Large Scale Attacks. 2021 IEEE International Conference on Communications Workshops (ICC Workshops). :1–6.
Distributed denial of service attacks threaten the security and health of the Internet. Remediation relies on up-to-date and accurate attack signatures. Signature-based detection is relatively inexpensive computationally. Yet, signatures are inflexible when small variations exist in the attack vector. Attackers exploit this rigidity by altering their attacks to bypass the signatures. Our previous work revealed a critical problem with conventional machine learning models. Conventional models are unable to generalize on the temporal nature of network flow data to classify attacks. We thus explored the use of deep learning techniques on real flow data. We found that a variety of attacks could be identified with high accuracy compared to previous approaches. We show that a convolutional neural network can be implemented for this problem that is suitable for large volumes of data while maintaining useful levels of accuracy.
Allagi, Shridhar, Rachh, Rashmi, Anami, Basavaraj.  2021.  A Robust Support Vector Machine Based Auto-Encoder for DoS Attacks Identification in Computer Networks. 2021 International Conference on Intelligent Technologies (CONIT). :1–6.
An unprecedented upsurge in the number of cyberattacks and threats is the corollary of ubiquitous internet connectivity. Among a variety of threats and attacks, Denial of Service (DoS) attacks are crucial and conventional mechanisms currently being used for detection/ identification of these attacks are not adequate. The use of real-time and robust mechanisms is the way to handle this. Machine learning-based techniques have been extensively used for this in the recent past. In this paper, a robust mechanism using Support Vector Machine Based Auto-Encoder is proposed for identifying DoS attacks. The proposed technique is tested on the CICIDS dataset and has given 99.32 % accuracy for DoS attacks. To study the effect of the number of features on the performance of the technique, a discriminant component analysis is deployed for feature reduction and independent experiments, namely SVM with 25 features, SVM with 30 features, SVM with 35 features, and PCA-SVM with 25 features, are conducted. From the experiments, it is observed that AE-SVM has performed better than others.
Stan, Orly, Bitton, Ron, Ezrets, Michal, Dadon, Moran, Inokuchi, Masaki, Ohta, Yoshinobu, Yagyu, Tomohiko, Elovici, Yuval, Shabtai, Asaf.  2021.  Heuristic Approach for Countermeasure Selection Using Attack Graphs. 2021 IEEE 34th Computer Security Foundations Symposium (CSF). :1–16.
Selecting the optimal set of countermeasures to secure a network is a challenging task, since it involves various considerations and trade-offs, such as prioritizing the risks to mitigate given the mitigation costs. Previously suggested approaches are based on limited and largely manual risk assessment procedures, provide recommendations for a specific event, or don't consider the organization's constraints (e.g., limited budget). In this paper, we present an improved attack graph-based risk assessment process and apply heuristic search to select an optimal countermeasure plan for a given network and budget. The risk assessment process represents the risk in the system in such a way that incorporates the quantitative risk factors and relevant countermeasures; this allows us to assess the risk in the system under different countermeasure plans during the search, without the need to regenerate the attack graph. We also provide a detailed description of countermeasure modeling and discuss how the countermeasures can be automatically matched to the security issues discovered in the network.
Bardhan, Shuvo, Battou, Abdella.  2021.  Security Metric for Networks with Intrusion Detection Systems having Time Latency using Attack Graphs. 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC). :1107–1113.
Probabilistic security metrics estimate the vulnerability of a network in terms of the likelihood of an attacker reaching the goal states (of a network) by exploiting the attack graph paths. The probability computation depends upon several assumptions regarding the possible attack scenarios. In this paper, we extend the existing security metric to model networks with intrusion detection systems and their associated uncertainties and time latencies. We consider learning capabilities of attackers as well as detection systems. Estimation of risk is obtained by using the attack paths that are undetectable owing to the latency of the detection system. Thus, we define the overall vulnerability (of a network) as a function of the time window available to an attacker for repeated exploring (via learning) and exploitation of a network, before the attack is mitigated by the detection system. Finally, we consider the realistic scenario where an attacker explores and abandons various partial paths in the attack graph before the actual exploitation. A dynamic programming formulation of the vulnerability computation methodology is proposed for this scenario. The nature of these metrics are explained using a case study showing the vulnerability spectrum from the case of zero detection latency to a no detection scenario.
Ngo, Quoc-Dung, Nguyen, Huy-Trung, Nguyen, Viet-Dung, Dinh, Cong-Minh, Phung, Anh-Tu, Bui, Quy-Tung.  2021.  Adversarial Attack and Defense on Graph-based IoT Botnet Detection Approach. 2021 International Conference on Electrical, Communication, and Computer Engineering (ICECCE). :1–6.
To reduce the risk of botnet malware, methods of detecting botnet malware using machine learning have received enormous attention in recent years. Most of the traditional methods are based on supervised learning that relies on static features with defined labels. However, recent studies show that supervised machine learning-based IoT malware botnet models are more vulnerable to intentional attacks, known as an adversarial attack. In this paper, we study the adversarial attack on PSI-graph based researches. To perform the efficient attack, we proposed a reinforcement learning based method with a trained target classifier to modify the structures of PSI-graphs. We show that PSI-graphs are vulnerable to such attack. We also discuss about defense method which uses adversarial training to train a defensive model. Experiment result achieves 94.1% accuracy on the adversarial dataset; thus, shows that our defensive model is much more robust than the previous target classifier.
Gong, Jianhu.  2021.  Network Information Security Pipeline Based on Grey Relational Cluster and Neural Networks. 2021 5th International Conference on Computing Methodologies and Communication (ICCMC). :971–975.
Network information security pipeline based on the grey relational cluster and neural networks is designed and implemented in this paper. This method is based on the principle that the optimal selected feature set must contain the feature with the highest information entropy gain to the data set category. First, the feature with the largest information gain is selected from all features as the search starting point, and then the sample data set classification mark is fully considered. For the better performance, the neural networks are considered. The network learning ability is directly determined by its complexity. The learning of general complex problems and large sample data will bring about a core dramatic increase in network scale. The proposed model is validated through the simulation.
2021-12-22
Panda, Akash Kumar, Kosko, Bart.  2021.  Bayesian Pruned Random Rule Foams for XAI. 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). :1–6.
A random rule foam grows and combines several independent fuzzy rule-based systems by randomly sampling input-output data from a trained deep neural classifier. The random rule foam defines an interpretable proxy system for the sampled black-box classifier. The random foam gives the complete Bayesian posterior probabilities over the foam subsystems that contribute to the proxy system's output for a given pattern input. It also gives the Bayesian posterior over the if-then fuzzy rules in each of these constituent foams. The random foam also computes a conditional variance that describes the uncertainty in its predicted output given the random foam's learned rule structure. The mixture structure leads to bootstrap confidence intervals around the output. Using the Bayesian posterior probabilities to prune or discard low-probability sub-foams improves the system's classification accuracy. Simulations used the MNIST image data set of 60,000 gray-scale images of ten hand-written digits. Dropping the lowest-probability foams per input pattern brought the pruned random foam's classification accuracy nearly to that of the neural classifier. Posterior pruning outperformed simple accuracy pruning of a random foam and outperformed a random forest trained on the same neural classifier.
2021-12-21
Zhang, Pengfeng, Tian, Chuan, Shang, Tao, Liu, Lin, Li, Lei, Wang, Wenting, Zhao, Yiming.  2021.  Dynamic Access Control Technology Based on Zero-Trust Light Verification Network Model. 2021 International Conference on Communications, Information System and Computer Engineering (CISCE). :712–715.
With the rise of the cloud computing and services, the network environments tend to be more complex and enormous. Security control becomes more and more hard due to the frequent and various access and requests. There are a few techniques to solve the problem which developed separately in the recent years. Network Micro-Segmentation provides the system the ability to keep different parts separated. Zero Trust Model ensures the network is access to trusted users and business by applying the policy that verify and authenticate everything. With the combination of Segmentation and Zero Trust Model, a system will obtain the ability to control the access to organizations' or industrial valuable assets. To implement the cooperation, the paper designs a strategy named light verification to help the process to be painless for the cost of inspection. The strategy was found to be effective from the perspective of the technical management, security and usability.
Kowalski, Dariusz R., Mosteiro, Miguel A..  2021.  Time and Communication Complexity of Leader Election in Anonymous Networks. 2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS). :449–460.
We study the problem of randomized Leader Election in synchronous distributed networks with indistinguishable nodes. We consider algorithms that work on networks of arbitrary topology in two settings, depending on whether the size of the network, i.e., the number of nodes \$n\$, is known or not. In the former setting, we present a new Leader Election protocol that improves over previous work by lowering message complexity and making it close to a lower bound by a factor in \$$\backslash$widetildeO($\backslash$sqrtt\_mix$\backslash$sqrt$\backslash$Phi)\$, where $\Phi$ is the conductance and \textsubscriptmix is the mixing time of the network graph. We then show that lacking the network size no Leader Election algorithm can guarantee that the election is final with constant probability, even with unbounded communication. Hence, we further classify the problem as Leader Election (the classic one, requiring knowledge of \$n\$ - as is our first protocol) or Revocable Leader Election, and present a new polynomial time and message complexity Revocable Leader Election algorithm in the setting without knowledge of network size. We analyze time and message complexity of our protocols in the CONGEST model of communication.
2021-12-20
Yang, Yuhan, Zhou, Yong, Wang, Ting, Shi, Yuanming.  2021.  Reconfigurable Intelligent Surface Assisted Federated Learning with Privacy Guarantee. 2021 IEEE International Conference on Communications Workshops (ICC Workshops). :1–6.
In this paper, we consider a wireless federated learning (FL) system concerning differential privacy (DP) guarantee, where multiple edge devices collaboratively train a shared model under the coordination of a central base station (BS) through over-the-air computation (AirComp). However, due to the heterogeneity of wireless links, it is difficult to achieve the optimal trade-off between model privacy and accuracy during the FL model aggregation. To address this issue, we propose to utilize the reconfigurable intelligent surface (RIS) technology to mitigate the communication bottleneck in FL by reconfiguring the wireless propagation environment. Specifically, we aim to minimize the model optimality gap while strictly meeting the DP and transmit power constraints. This is achieved by jointly optimizing the device transmit power, artificial noise, and phase shifts at RIS, followed by developing a two-step alternating minimization framework. Simulation results will demonstrate that the proposed RIS-assisted FL model achieves a better trade-off between accuracy and privacy than the benchmarks.