Reviewerly/exHarmony huggingface dataset:
Protecting Virtual Machines against Untrusted Hypervisor on ARM64 Cloud Platform

In cloud computing, the confidentiality and integrity of virtual machines (VMs) are facing severe threats because of the huge trusted computing base (TCB) software stack in virtualization layer. With the increasing momentum of ARM64 in cloud computing server markets, it is important to protect VMs from privileged software (including host operating system and hypervisor) on ARM64. In this paper, we have created SecureHyp, a new virtualization platform design for refactoring the existing hypervisor using the ARM64 hardware security mechanisms to reduce the TCB while protecting VMs against untrusted privileged software. Based on the principle of the least privilege, SecureHyp separates the sensitive-resource management from the rights of the hypervisor and prohibits the hypervisor from accessing specific sensitive resources. By deploying the memory isolation using ARM Trusted Firmware (ATF) and virtual Memory Management Unit (vMMU), SecureHyp ensures both the security and efficiency of the guest VMs. We have implemented SecureHyp on Linux firefly-4.4.194 with modest modification. The results show that SecureHyp can protect the confidentiality and integrity of virtual machines with only around 2000 lines of code software TCB and negligible performance overhead.
Analysis of DRAM Vulnerability Using Computation Tree Logic

Shared resources facilitate both side and covert channels and greatly endanger information security even in cloud environments. In cloud computing environments, multiple tenants often reside on the same multi-processor system. Therefore, it is important to prevent information leakage between tenants. Shared memory between tenants is usually disabled for security reasons. In addition, tenants typically do not share physical CPUs. In this case, cache attacks do not work. As a common shared resource, DRAM memory can also be exploited as a source of side and covert channels.In this paper, Computation Tree Logic (CTL) is used to model the behaviors of row buffer logic in DRAM and derive all possible timing-based vulnerabilities. The problem of state space explosion is alleviated by using bounded model checking in this method. In total, our method derives 24 kinds of DRAM timing-based vulnerabilities. Furthermore, we analyze DRAM vulnerabilities to help engineers understand them and take corresponding measures in the design according to derived security specifications.
Asynchronous Federated Learning in Decentralized Topology Based on Dynamic Average Consensus

Federated learning (FL) is an emerging technique to tackle the problem of isolated data islands. Vanilla FL usually relies on a centralized topology with a synchronous communication setup, where a parameter server coordinates the distributed users by periodically receiving the model updates from them, performing the aggregation to build a global model and sending back the aggregated model to those users. These restrictions however make FL become vulnerable to the single point failure and not efficient enough given the heterogeneous device capabilities and communication conditions. This paper sheds light on enabling FL in a decentralized topology with an asynchronous communication setup. We first incorporate the asynchronous first-order dynamic average consensus into FL and propose an underlying Async-DFL algorithm. Then, to alleviate the staleness effect in Async-DFL, we introduce a hyper-parameter into Async-DFL and further devise an optimized hyper-mix Async-DFL solution. The experimental result demonstrates the feasibility of these approaches and shows that the hyper-mix solution performs better than the underlying Async-DFL.
Realizing Health 4.0 in Beyond 5G Networks

The advancements of Edge and Internet of Things (IoT) devices in terms of their processing, storage and communication capabilities, in addition to the advancements in wireless communication and networking technologies, have led to the rise in Intelligent Edge-enabled IoT architectures. Federated Learning (FL) is one example in which intelligence is adapted to the edge to offload some of the processing load from centralized entities and maintain secure localized model training. With Health 4.0, it is anticipated that distributed and edge-supported Artificial Intelligence (AI) will enable faster and more accurate early-stage disease discovery that relies significantly on intelligent remote and on-site IoT devices. Given that healthcare systems are highly scrutinized by both governments and patients to maintain high levels of data privacy and security, FL coupled with the support of blockchain will provide an optimal solution to reinforce today's healthcare frameworks. In this paper, we propose a FL-enabled framework for healthcare systems that is supported by edge-computing, blockchain and intelligent IoT devices. The solution considers a pneumonia detection use-case as a proof-of-concept and is applicable to an extended set of health-related use-cases. Different pre-trained models are compared against the proposed FL-supported model, namely, CNN, GG16, VGG19, InceptionV3, ResNet, DenseNet, and Xception. Results show high model accuracy attainment and significant improvements in terms of data privacy.
How Does FAW Attack Impact an Imperfect PoW Blockchain: A Simulation-based Approach

Malignant miners with small computing power can achieve unfair revenue and degrade system throughput through launching Fork after withholding (FAW) attack in a Proof-of-Work (PoW) blockchain system. The existing works about FAW attack have some of the following issues: (i) only studying Bitcoin blockchain, (ii) assuming that the blockchain network is perfect and then ignoring forks due to block propagation delay, and (iii) assuming that there is only one pool under attack. This paper attempts to investigate FAW attack in imperfect Bitcoin and Ethereum networks where malicious miners attack multiple victim pools. We develop a simulator to capture the chain dynamics under FAW attack in a PoW system where the longest-chain protocol is used. Two different computing power allocation strategies for malicious miners, PAS and EAS, are investigated in terms of the profitability of FAW adversaries, the loss of victims, and the blockchain throughput. The results reveal that FAW adversaries can get more revenue under PAS when more victim pools are subjected to attack in both Bitcoin and Ethereum. If FAW adversaries adopt EAS and the number of victims vary from 1 to 12, they can get maximal revenue when attack 7 victims in Bitcoin. The blockchain throughput decreases significantly under PAS while it is almost unchanged under EAS with the increasing number of victims in both Bitcoin and Ethereum. Our work helps the design of countermeasures against FAW attack.
Spatio-Temporal Federated Learning for Massive Wireless Edge Networks

This paper presents a novel approach to conduct highly efficient federated learning (FL) over a massive wireless edge network, where an edge server and numerous mobile devices (clients) jointly learn a global model without transporting the huge amount of data collected by the mobile devices to the edge server. The proposed FL approach is referred to as spatio-temporal FL (STFL), which jointly exploits the spatial and temporal correlations between the learning updates from different mobile devices scheduled to join STFL in various training epochs. The STFL model not only represents the realistic intermittent learning behavior from the edge server to the mobile devices due to data delivery outage, but also features a mechanism of compensating loss learning updates in order to mitigate the impacts of intermittent learning. An analytical framework of STFL is proposed and employed to study the learning capability of STFL via its convergence performance. In particular, we have assessed the impact of data delivery outage, intermittent learning mitigation, and statistical heterogeneity of datasets on the convergence performance of STFL. The results provide crucial insights into the design and analysis of STFL-based wireless networks.
Securing E-Petition: A Privacy-Preserving Fine-Grained Electronic Petition System for Health and Political Petitions

E-petition has played an important role in health and politics that collects public opinions and requests a superior or an authority to take actions towards a health or political problem. However, this activity exposes the privacy of the signers who participate to express opinions. In this paper, we propose a privacy-preserving fine-grained e-petition system that supports attribute-based identity verification for signers, while protecting their privacy. By considering the target groups of signers in a specific health or political petition, an attribute policy is defined to ensure that only the signers with the attributes that satisfy the attribute policy can sign the petition. The fine-grained petition is better than the traditional e-petitions because it can improve the trustworthiness of the petition results via proactive signer selection. Moreover, the new petition system protects the identities of the signers by using the non-interactive zero-knowledge proof system, such that the signers are anonymous in signing petitions. In addition, the proposed petition system supports the tracing of double-signing, a cheating behavior that an anonymous signer can submit more than one signature in a petition without being detected. Finally, we prove that the proposed petition system achieves the desirable security properties, including anonymity, unforgeability, and traceability, and demonstrate that the system is efficient to be implemented on the mobile devices.
ABBA: Advance Block Body Agreement for Scaling Blockchain Networks

Scalability is one of the main issues of blockchains that limits its applications. Blockchain throughput (i.e., transactions per second) is constrained by its block size, and block generation rate is restricted by consensus time and the network size. Despite many scaling approaches available, most of them are either not applicable to existing blockchains, or oblivious to blockchain applications. We propose a novel blockchain network scalability mechanism, Advance Block Body Agreement (ABBA), that uses pre-agreement on an upcoming block body to reduce consensus time without changing the consensus algorithm itself. A block size of a blockchain is limited since a large block size causes higher consensus time and unnecessary transient forks, resulting in an unstable network. By reducing consensus time, ABBA allows increasing a blockchain's block size that leads to a higher throughput. ABBA also reduces transient forks in blockchain networks reducing unnecessary network overhead. ABBA can be used with any consensus protocol without changing consensus or its application layer. It can also be used with other existing scalability approaches complementarily. We have built an efficient blockchain network simulator and conducted extensive evaluations under various network conditions. The results show that an ABBA-enabled blockchain network has a significantly higher throughput and lower transient forks than an original base blockchain network.
Privacy-preserving Training Algorithm for Naive Bayes Classifiers

The growing popularity of Machine learning (ML) that appreciates high quality training datasets collected from multiple organizations raises natural questions about the privacy guarantees that can be provided in such settings. Our work tackles this problem in the context of multi-party secure ML wherein multiple organizations provide their sensitive datasets to a data user and train a Naive Bayes (NB) model with the data user. We propose PPNB, a privacy-preserving scheme for training NB models, based on Homomorphic Cryptosystem (HC) and Differential Privacy (DP). PPNB achieves a balance performance between efficiency and accuracy in multi-party secure ML, enabled flexible switch among different tradeoffs by parameter tuning. Extensive experimental results validate the effectiveness of PPNB.
DPLogChain: A Dynamic Packaging LogChain based on Random Witness

The log blockchain utilizes the append-only and tamper-proof characteristics of the blockchain to ensure the consistency and credibility of log data. However, the mismatch between log system and log blockchain in throughput and storage limits the extensive use of log blockchain. This paper proposed DPLogChain, a dynamic packaging log blockchain based on random witness, which randomly packages log data with different granularity according to its security level to match the throughput of both systems and then stores proof data on chain. Meanwhile, the real-time processing avoids the possibility of collusion and tampering between log system and log storage.
PipeCompress: Accelerating Pipelined Communication for Distributed Deep Learning

Distributed learning is widely used to accelerate the training of deep learning models, but it is known that communication efficiency limits the scalability of distributed learning systems. Current gradient compression techniques provide promising methods to reduce communication time, but the extra time incurred by compression is not negligible. After compression techniques are applied, the communication time is significantly reduced because the data size needed to communicate becomes much smaller, but compressing gradients is time-consuming and it becomes a new bottleneck. In this paper, we design and implement PipeCompress, a system to decouple compression and backpropagation operations into two processes and pipeline the two processes to hide compression time. We also propose a specialized inter-process communication mechanism based on the characteristics of DNN distributed training to improve the efficiency of passing messages between the two processes, which makes sure that the decoupling does not bring much extra inter-process communication time cost. As far as we know, this is the first work that notices the overhead of compression and pipelines backpropagation and compression operations to hide compression time in distributed learning. Experiments show that PipeCompress can significantly hide compression time, reduce iteration time, and accelerate the training process on various DNN models.
Why Riding the Lightning? Equilibrium Analysis for Payment Hub Pricing

Payment Channel Network (PCN) is an auspicious solution to the scalability issue of the blockchain, improving transaction throughput without relying on on-chain transactions. In a PCN, nodes can set prices for forwarding payments on behalf of other nodes, which motivates participation and improves network stability. Analyzing the price setting behaviors of PCN nodes plays a key role in understanding the economic properties of PCNs, but has been under-studied in the literature. In this paper, we apply equilibrium analysis to the price-setting game between two payment hubs in the PCN with limited channel capacities and partial overlap demand. We analyze existence of pure Nash Equilibriums (NEs) and bounds on the equilibrium revenue under various cases, and propose an algorithm to find all pure NEs. Using real data, we show bounds on the price of anarchy/stability and average transaction fee under realistic network conditions, and draw conclusions on the economic advantage of the PCN for making payment transfers by cryptocurrency users.
Real-time Identification of VPN Traffic based on Counting Bloom Filter and Chained Hash Table from Sampled Data in High-speed Networks

Virtual Private Network (VPN) can bypass censorship and access services that are geographically locked. Therefore, VPN traffic identification has become an urgent problem in traffic classification. The existing VPN traffic identification methods use complete traffic for analysis. However, massive data analysis in high-speed networks consumes many resources, limiting the real-time processing of traffic identification. The management of high-speed networks is mainly based on sampled traffic. As VPN traffic accounts for a relatively low proportion, it is particularly challenging to identify VPN traffic from sampled data. This paper proposes a real-time identification method for VPN traffic from sampled data in high-speed networks. In our method, we construct features that are still available after sampling and design a fast traffic processing structure based on Counting Bloom Filter and Chained Hash Table (CBCH). To validate the usability of our method, we use 900 seconds of traffic generated on a 10 Gbps link as background traffic, mixed with V2Ray traffic, which is increasingly common in VPN traffic. With the VPN traffic proportion of 0.03%, it takes only 50.79 seconds to complete the processing at the sampling ratio of 1/256. This time is significantly less than the traffic generation time. For the effective flows extracted from sampled backbone traffic, the identification results are maintained at a high level with 97% precision, 93% recall, and 95% F1 score. In addition, our method can achieve fine-grained VPN traffic identification of different V2Ray tools.
From adversarial examples to data poisoning instances: utilizing an adversarial attack method to poison a transfer learning model

Despite the wide-ranging applicability of machine learning methods, they are vulnerable to security attacks, such as evasion attacks and triggerless data poisoning attacks. An evasion attack occurs at the inference time when an attacker feeds in an adversarial example, a malicious perturbed input that appears the same as its untampered copy to a human oracle. In contrast, a triggerless data poisoning attack occurs at training time. An attacker tries to subvert learning with injected poisoned instances. In this research, we focus on a special sub-category of data poisoning attacks, namely triggerless clean-label targeted data poisoning attacks. This type of attacks is more realistic in the sense that it does not require an attacker to have the ability to change the label of any training instance. That is, attackers can successfully attack the training process with a poison instance that is correctly labeled by a human oracle. We propose a simple but effective way to alter an adversarial attack method into a triggerless clean-label targeted data poisoning attack method with a remarkable attack success rate. Furthermore, our proposed method only requires the injection of a single poison instance to manipulate a transfer learning model to misclassify an untampered targeted instance. We compare our method with a popular one-shot attack and show that our method is easier to be used as we do not need to tune for a hyperparameter such as a similarity coefficient.
P T-symmetric Bayesian parameter estimation on a superconducting quantum processor

To realize the computational advantages of quantum mechanics, it is necessary to be able to effectively perform quantum state tomography, and Bayesian methods provide significant opportunities in this regard, being applicable in the uncertainty regimes, where the local approaches based on Cramer-Rao bound become ill-defined. ${\mathcal{P}}{\mathcal{T}}$ -symmetric quantum mechanics provides a unique advantage for Bayesian parameter estimation having an additional degree of freedom, absent in the regular Hermitian case, which significantly enhances the quantum Fisher information and reduces variance of the obtained results. While previous studies have examined ${\mathcal{P}}{\mathcal{T}}$ -symmetric Bayesian parameter estimation in theory, we conduct experimental studies using IBM Quantum Experience and compare them with theoretical calculations. In our approach, the evolution of a qubit controlled by a ${\mathcal{P}}{\mathcal{T}}$-symmetric Hamiltonian is realized by the dilation method using ancilla qubit. We show that our implementation is consistent with the theoretical model and, in particular, we observe a good correspondence between the theoretical and experimental likelihood distributions. Thus we bridge the gap between theory and practical implementations in ${\mathcal{P}}{\mathcal{T}}$-symmetric quantum mechanics and its practical applications in quantum information science.
Quantum Key Distribution: An Optimization Approach for the Management Plane

As the number of attacks increases in optical networks, security at the management layer (ML) of these networks is of paramount importance to ensure that critical network management information is securely transmitted between ML node(s) and the rest of the network nodes. This work considers the problem of developing a quantum key distribution (QKD) framework for the management layer of an optical network, so that a set of management nodes can securely communicate and exchange keys with the network nodes. An integer linear program (ILP) is formulated for the design of a management layer - quantum key distribution (ML-QKD) network, aiming to maximize the transmission rates provided to network and management nodes, by optimally selecting the placement of the management node(s) and QKD systems. Performance results for several scenarios with a different number of available management nodes and QKD systems, demonstrate that the proposed formulation can design an optimal ML-QKD network.
In-Network Caching and Learning Optimization for Federated Learning in Mobile Edge Networks

In this paper, we develop a novel privacy-aware framework to address straggling problem in a federated learning (FL)-based mobile edge network through maximizing profit for the mobile service provider (MSP). In particular, unlike the conventional FL process when participating mobile users (MUs) have to train their all data locally, we propose a highly-effective solution that allows MUs to encrypt parts of local data and upload/cache the encrypted data to nearby mobile edge nodes (MENs) and/or a cloud server (CS) to perform additional training processes. In this way, we can not only mitigate the straggling problem caused by limited computing/communications resources at MUs but also enhance the usage efficiency of learning data from all MUs in the FL process. To optimize portions of encrypted data cached and trained at MENs/CS given constraints from MUs and the MSP while considering data privacy and training costs, we first formulate the profit maximization problem for the MSP as an optimal in-network encrypted data caching and learning optimization. We then prove that the objective function is concave, and thus an interior-point method algorithm can be effectively adopted to quickly find the optimal solution. The numerical results demonstrate that our proposed framework can enhance the profit of the MSP up to 5.39 times compared with other FL methods.