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The following are those that were assigned reviews between November 2022 and October 2023. [ABSTRACT FROM AUTHOR]

Local differential privacy (LDP) is a promising technique to realize privacy-preserving data aggregation without a trusted aggregator. Normally, an LDP protocol requires each user to locally perturb his raw data and submit the perturbed data to the aggregator. Consequently, LDP is vulnerable to output poisoning attacks. Malicious users can skip the perturbation and submit carefully crafted data to the aggregator, altering the data aggregation results. Existing verifiable LDP protocols, which can verify the perturbation process and prevent output poisoning attacks, usually incur significant computation and communication costs, due to the use of zero-knowledge proofs. In this paper, we analyze the attacks on two classic LDP protocols for frequency estimation, namely GRR and OUE, and propose two verifiable LDP protocols. The proposed protocols are based on an interactive framework, where the user and the aggregator complete the perturbation together. By providing some additional information, which reveals nothing about the raw data but helps the verification, the user can convince the aggregator that he is incapable of launching an output poisoning attack. Simulation results demonstrate that the proposed protocols have good defensive performance and outperform existing approaches in terms of efficiency. [ABSTRACT FROM AUTHOR]

This article presents a learning-based controller to solve the cooperative formation control problem for multiagent system (MAS) with collision avoidance. First, the consensus problem of first-order MAS is mostly solved by linear matrix inequality (LMI) without consideration of energy loss. To overcome these difficulties, an adaptive dynamic programming (ADP) technique is fit to solve the consensus problem and similar formation control problem for second-order MAS by the establishment of a performance index function. Besides, we introduce the generalized policy iteration (GPI) algorithm as a kind of ADP technique without the problem of low convergence speed and high computational complexity. Combined with previous works, it can be found that our proposed structure can be extended to high-order cases based on the structure of local neighborhood formation error and algorithm. Afterward, the convergence analysis, optimality analysis, and stability analysis are given. Neural networks (NNs) are also implemented to approximate the iterative control policies and value functions, respectively. Moreover, we realize that many collisions may occur in the formation control problem. Inspired by the idea of the artificial potential field (APF) technique, the concept of the repulsive force field is introduced based on our proposed learning-based structure to avoid collisions simply and efficiently. Finally, a simulation is provided to demonstrate the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

Network function virtualization is a key enabling technology in future wireless networks for flexible and efficient sharing of network resources. Due to the increasing heterogeneity of network resource providers, a blockchain-based distributed architecture is a promising solution to enable reliable and transparent virtualized network function (VNF) management. However, since on-chain storage and computation are costive, it becomes a challenging task to achieve efficient VNF management with blockchain. In this paper, we first introduce a consortium blockchain for collaborative VNF management among network resource providers. Then, we propose an authenticated VNF dictionary that can be stored as a succinct authenticator on blockchain to support rich VNF query functionalities and efficient verifications of query results. Moreover, we design a dictionary pruning strategy to securely generate a compact authenticator for a given query, which reduces unnecessary memory accesses of the original dictionary when VNF queries are represented as arithmetic circuits. Finally, we conduct extensive experiments with a consortium blockchain network. The experimental results demonstrate that our pruning strategy is efficient for both on-chain and off-chain VNF management. [ABSTRACT FROM AUTHOR]

Humans are capable of managing multiple tasks simultaneously. It is widely assumed that human motor control can be emulated by impedance control. To achieve human-like behavior, however, the impedance parameters of multiple tasks may vary during task execution. We propose an algorithm that shapes task impedance as a function of the robot’s time-varying inertial properties. These properties involve virtually constrained masses and virtually constrained inertias that counteract a task in order to comply with a given constraint. In this work, we not only detect task conflicts, but also show how to handle them. Our method is able to control kinematically redundant robots. We developed a damping-design method that does not interfere with our desired Cartesian task-space behavior. The control approach was verified in experiments on a real robot. We compared our impedance shaping method with two alternative control approaches: simple impedance superposition and nullspace projection. Our method preserved the passivity while improving the Cartesian task performance of an impedance controller. The method has computational advantages, beneficial to control robots with many degrees of freedom. [ABSTRACT FROM AUTHOR]

Recent literature has explored the use of single-material piezoelectrics for impedance matching in electrically small strain antennas. Efficiency calculations have assumed that radiation resistance is the same as that of a small wire dipole. In this work, piezoelectric composites, or piezocomposites, are used for impedance matching electrically small antennas to increase the degrees of freedom in the design process. A methodology is proposed for impedance matching a piezocomposite strain antenna at the desired center frequency over the desired bandwidth via optimal choice of materials, volume fraction, and shape without the need for an external matching network. Furthermore, the radiation resistance for piezoelectric strain antennas is derived from Wheeler’s power factor and is shown to be much smaller than that of the similarly sized electric dipole antenna that was assumed in much of the literature. The radiated power and efficiency are expressed directly in terms of piezocomposite material properties and the dimensions of the antenna. The results from this investigation provide a model of the impedance matching capabilities and efficiency limits for the piezoelectric strain antenna, demonstrating fundamental limitations for VLF applications. [ABSTRACT FROM AUTHOR]

The development of Big Data and cloud computing has brought great progress to medical diagnosis and clinical services. However, as disease diagnosis technologies usually use lots of clinical medical data, patients’ privacy becomes increasingly important since the clinical results of medical data are much sensitive. In this article, to deal with the privacy preservation issue of patient’s medical data, we propose a privacy-preserving disease diagnosis scheme that is based on the Mahalanobis distance test, in which the role of the system consisting of query user (QU), aided cloud server (ACS), and classification cloud server (CCS) is to jointly calculate and protect the diagnosis data over the sensitive and outsourced medical data. In the diagnosis model, we outsource the clinical diagnosis process to be dealt with by the CCS, and then, the ACS can reduce the computational cost of the user side. We utilize the homomorphic re-encryption scheme to realize a secure computation over the outsourced medical data, and then, employ a secure multiplication (SM) protocol to implement the privacy-preserving Mahalanobis distance to output the disease diagnosis. Concretely, we provide an extended SM algorithm to solve the problem of multiplication of two encrypted data, and a minimum value comparison algorithm over ciphertext for comparing the encrypted Mahalanobis distance. Finally, we give the experimental performance in real data sheets, and the experimental results indicate that our scheme provides a lower computational cost in practical diagnosis services. [ABSTRACT FROM AUTHOR]

Due to the big data blowout from the Internet of Things and the rapid development of cloud computing, outsourcing computation has received considerable attention in recent years. Particularly, many outsourcing computation schemes have been proposed to dedicate the outsourcing polynomial computation due to its use in numerous fields, such as data analysis and machine learning. However, none of those schemes are practical enough, as they either require some time-consuming cryptographic operations to achieve fairness between the user and the worker, or cannot allow the user to outsource arbitrary polynomial to the worker, or need two non-collusive workers. To tackle these challenges, in this article, we propose a new outsourcing polynomial computation scheme by employing a variant of Horner’s method and the blockchain technology. Specifically, the former makes the computational cost on the worker side as low as possible, and the latter guarantees the fairness between the user and the worker if the result from the worker can be publicly verified. To achieve the public verifiability property, we apply the sampling technique, which is effective in our proposal according to a game-theoretic analysis. Furthermore, we also implement a prototype of our proposal and run it on an Ethereum test net. The extensive experimental results demonstrate that our proposal is efficient in terms of computational cost. [ABSTRACT FROM AUTHOR]

With the advent of 5G technology, the notion of latency got a prominent role in wireless connectivity, serving as a proxy term for addressing the requirements for real-time communication. As wireless systems evolve toward 6G, the ambition to immerse the digital into physical reality will increase. Besides making the real-time requirements more stringent, this immersion will bring the notions of time, simultaneity, presence, and causality to a new level of complexity. A growing body of research points out that latency is insufficient to parameterize all real-time requirements. Notably, one such requirement that received significant attention is information freshness, defined through the Age of Information (AoI) and its derivatives. In general, the metrics derived from a conventional black-box approach to communication network design are not representative of new distributed paradigms, such as sensing, learning, or distributed consensus. The objective of this article is to investigate the general notion of timing in wireless communication systems and networks, and its relation to effective information generation, processing, transmission, and reconstruction at the senders and receivers. We establish a general statistical framework of timing requirements in wireless communication systems, which subsumes both latency and AoI. The framework is made by associating a timing component with the two basic statistical operations: decision and estimation. We first use the framework to present a representative sample of the existing works that deal with timing in wireless communication. Next, it is shown how the framework can be used with different communication models of increasing complexity, starting from the basic Shannon one-way communication model and arriving at communication models for consensus, distributed learning, and inference. Overall, this article fills an important gap in the literature by providing a systematic treatment of various timing measures in wireless communication and sets the basis for design and optimization for the next-generation real-time systems. [ABSTRACT FROM AUTHOR]

This brief introduces the concept of anonymous communication into multi-agent systems and proposes a new solution to deal with the trade-off between privacy and accuracy in privacy-preserving consensus. Furthermore, mixed encryption is applied into multi-agent systems for the first time which provides a safe and high-efficient communication. Specifically, asymmetric encryption is used at the beginning of communication to guarantee the secure exchange of initial values and a new symmetric encryption approach, called state difference, is designed and used in the following process to reduce the high cost of computing and communicating in asymmetric encryption. Combining the above methods, anonymous mixed encryption communication protocol for multi-agent systems to reach privacy-preserving consensus is finally proposed. The numerical simulation shows the effectiveness of the proposed communication protocol. [ABSTRACT FROM AUTHOR]

We introduce Info-Commit, an information-theoretic protocol for polynomial commitment and verification. With the help of a trusted initializer, a succinct commitment to a private polynomial $f$ is provided to the user. The user then queries the server to obtain evaluations of $f$ at several inputs chosen by the user. The server provides the evaluations along with proofs of correctness which the user can verify against the initial commitment. Info-Commit has four main features. Firstly, the user is able to detect, with high probability, if the server has responded with evaluations of the same polynomial initially committed to. Secondly, Info-Commit provides rigorous privacy guarantees for the server: upon observing the initial commitment and the response provided by the server to $m$ evaluation queries, the user only learns $O(m^{2})$ symbols about the coefficients of $f$. Thirdly, the verifiability and the privacy guarantees are unconditional regardless of the computational power of the two parties. Lastly, Info-Commit is doubly-efficient in the sense that in the evaluation phase, the user runs in $O(\sqrt {d})$ time and the server runs in $O(d)$ time, where $d-1$ is the degree of the polynomial $f$. [ABSTRACT FROM AUTHOR]

There is no design template more important than DNA. Within the sequences of this exquisite substance lie the design plans for each of us and for every living organism. Shaped over billions of years by the creative machinations of evolution, this design template encodes the most complex dynamical systems known to us. Yet, it is only in our lifetimes that we are able to directly edit this template and engineer our own designs. The story that I tell in this article is about our early attempts to design and commission our own control systems in living cells. Guided by what we have learned from controlling man-made systems, we are beginning to develop the theory and methodologies needed to build control systems at the molecular level, an endeavor that is as challenging as it is rewarding. If carried out responsibly, this new ability to reshape the DNA template can have a tremendous benefit for our health and well-being, and will drive major advances in basic science, industrial biotechnology, and medical therapy. In this article, I will take the readers of the proceedings on a journey through the new and promising world of rationally designed genetic control systems. Using a minimum of jargon, I will introduce them to the biological concepts needed to develop an understanding and appreciation of the main design concepts emerging in this nascent area of research. My goal is to convey my own sense of excitement about the possibilities, but it is also to impart a feeling of the opportunities that lay ahead for members of the IEEE to contribute with their own creative ideas to the shaping of this most versatile of design templates, the DNA. [ABSTRACT FROM AUTHOR]

Metabolic network analysis is an accessible and versatile modeling approach for biology that has taken much inspiration from electric circuit analysis. After introducing its main concepts, we focus on numerical tools, such as optimization and sampling, to predict cellular features and behaviors at a large scale. Optimization approaches exploit that metabolic networks are shaped by evolution and are, thus, assumed to embed a fitness condition reflecting the environment that they evolved in. In the past ten years, there is a trend to generalize metabolic network analysis to consortia of interacting species. This raises technical questions on, for example, optimality in consortia but also more general ones on metabolic coevolution, information exchange, and adaptation. This suggests and allows us to explore interesting analogies to technological systems, specifically to smart grids. [ABSTRACT FROM AUTHOR]

It is inevitable and evident that outsourcing complicated intensive tasks to public cloud vendors would be the primary option for resource-constrained clients in order to save cost. Unfortunately, the public cloud vendors are usually untrusted. They may inadvertently leak the data or misuse the user’s data, compromise user’s privacy or intentionally corrupt computational results to make the system unreliable. It is therefore important how to stop this happening whilst embracing the computational power of public cloud vendors. Non-negative matrix factorization (NMF) is a significant method for conducting data dimension reduction, which has been widely used in large-scale data processing. Nevertheless, due to its non-polynomial hardness, NMF cannot be conducted efficiently using local computation resources, especially when dealing with big data. Motivated by this issue, we address this by presenting a novel outsourced scheme for NMF (O-NMF), which aims to lessen clients’ computing burden and tackle secure problems faced by outsourcing NMF. Particularly, based on two non-collusion servers, O-NMF exploits Paillier homomorphism to preserve data privacy. Additionally, O-NMF allows a verification mechanism to assist clients in verifying returned results with high probability. Security analysis and experimental evaluation demonstrates that the validity and practicality of O-NMF is also provided in this work. [ABSTRACT FROM AUTHOR]

In cryptography and algorithmic number theory, modular inversion is viewed as one of the most common and time-consuming operations. It is hard to be directly accomplished on resource-constrained clients (e.g., mobile devices and IC cards) since modular inversion involves a great amount of operations on large numbers in practice. To address the above problem, this paper proposes a novel unimodular matrix transformation technique to realize secure outsourcing of modular inversion. This technique makes our algorithm achieve several amazing properties. First, to the best of our knowledge, it is the first secure outsourcing computation algorithm that supports arbitrary and variable modulus, which eliminates the restriction in previous work that the protected modulus has to be a fixed composite number. Second, our algorithm is based on the single untrusted program model, which avoids the non-collusion assumption between multiple servers. Third, for each given instance of modular inversion, it only needs one round interaction between the client and the cloud server, and enables the client to verify the correctness of the results returned from the cloud server with the (optimal) probability 1. Furthermore, we propose an extended secure outsourcing algorithm that can solve modular inversion in multi-variable case. Theoretical analysis and experimental results show that our proposed algorithms achieve remarkable local-client’s computational savings. At last, as two important and helpful applications of our algorithms, the outsourced implementations of the key generation of RSA algorithm and the Chinese Reminder Theorem are given. [ABSTRACT FROM AUTHOR]

In-band full-duplex (IBFD) technology is promised to deliver us from the scarcity of spectral resources as well as create novel multifunction wireless systems. This concept was initially investigated in the 1960s for continuous-wave radar applications , and, in the past decade, it has undergone a shift in research toward communications – and converged RF systems ,. Since 2010, more than 600 IEEE publications alone have focused on at least one aspect of the technology, including more than 400 conference papers, 200 journal articles, and 20 magazine features. After digesting these numbers, it is fair to ask: Why has IBFD functionality not been incorporated into a wireless standard yet? [ABSTRACT FROM AUTHOR]

Many in-band full-duplex (IBFD) applications require omnidirectional radiation coverage in addition to multilayered self-interference cancellation (SIC). While system-level SIC approaches often require high-isolation antennas before implementing analog and/or digital methods, maintaining the desired omnidirectional transmit/receive patterns is difficult for compact designs that utilize the same polarization. This article discusses the concept development and design progression of the circular mode phasing technique applied to omnidirectional IBFD antenna arrays. The theoretical fundamentals are presented along with simulation results and then validated through the discussion of two prototype antennas, which were measured to provide up to 60 dB of isolation over 2.4–2.5 GHz, which is the highest reported for this SIC method. Additionally, the evolution of this concept for a collection of measured prototypes is summarized and characterized with a unique figure of merit, which should help advance the application of this technique within future IBFD systems. [ABSTRACT FROM AUTHOR]

We comprehensively discuss state-of-the-art integrated guidance and control (IGC) systems, in applications ranging from guided missiles to unmanned vehicles. Unlike separate guidance and control systems, IGC systems consider both control and guidance loops simultaneously, taking into account the cross-coupling relations and the limitations between the two loops. We highlight the pros and cons of the existing IGC algorithms while pointing out several research opportunities and potential research challenges associated with each technique. We envisage that future research direction will be gradually shifted toward the use of artificial intelligence (AI), such as intelligent neuro-fuzzy systems, to achieve adaptive IGC systems that can perform intelligent self-learning and online optimization with minimum human intervention. Finally, this review also delivers an open message to encourage collaborations among experts from multiple disciplines, such as control systems, robotics, and machine learning. The development of multiple AI algorithms to tackle current issues in IGC systems will transform and create novel hybrid knowledge of intelligent IGC systems, paving the way to new applications in multiple robotic platforms. [ABSTRACT FROM AUTHOR]

The resilient consensus of multi-agent systems with trusted agents under switching topologies is addressed in this brief. A novel assumption on switching topologies is proposed, which is different from the existing works dependent on the classical graph robustness condition. Correspondingly, the trusted-region-based sliding-window weighted (TSW) algorithm is proposed to filter the received sampled data. It is successfully shown that the resilient consensus can be guaranteed under the assumption on switching topologies and TSW algorithm. Finally, numerical examples are conducted to validate the results. [ABSTRACT FROM AUTHOR]

As cloud computing becomes more popular, research has focused on usable solutions to the problem of verifiable computation (VC), where a computationally weak device (Verifier) outsources a program execution to a powerful server (Prover) and receives guarantees that the execution was performed faithfully. A Prover can further demonstrate knowledge of a secret input that causes the Verifier’s program to satisfy certain assertions, without ever revealing which input was used. State-of-the-art Zero-Knowledge Proofs of Knowledge (ZKPK) methods encode a computation using arithmetic circuits and preserve the privacy of Prover’s inputs while attesting the integrity of program execution. Nevertheless, developing, debugging, and optimizing programs as circuits remains a daunting task, as most users are unfamiliar with this programming paradigm. In this work, we present Zilch, a framework that accelerates and simplifies the deployment of VC and ZKPK for any application transparently, i.e., without the need of trusted setup. Zilch uses traditional instruction sequences rather than static arithmetic circuits that would need to be regenerated for each different computation. Towards that end, we have implemented Z MIPS: a MIPS-like processor model that allows verifying each instruction independently and compose a proof for the execution of the target application. To foster usability, Zilch incorporates a novel cross-compiler from an object-oriented Java-like language tailored to ZKPK and optimized our Z MIPS model, as well as a powerful API that enables integration of ZKPK within existing C/C++ programs. In our experiments, we demonstrate the flexibility of Zilch using two real-life applications, and evaluate Prover and Verifier performance on a variety of benchmarks. [ABSTRACT FROM AUTHOR]

Objective: Autonomic dysregulation in preterm infants requires continuous monitoring of vital signs such as heart rate over days to months. Unfortunately, common surface electrodes are prone to electrocardiography (ECG) signal artifacts and cause serious skin irritations during long-term use. In contrast, esophageal ECG is known to be very sensitive due to the proximity of electrodes and heart and insensitive to external influences. This study addresses if multichannel esophageal ECG qualifies for heart rate monitoring in preterm infants. Methods: We recorded esophageal leads with a multi-electrode gastric feeding tube in a clinical study with 13 neonates and compared the heartbeat detection performance with standard surface leads. A computationally simple and versatile ECG wave detection algorithm was used. Results: Multichannel esophageal ECG manifested heartbeat sensitivity and positive predictive value greater than 98.5% and significant less false negative (FN) ECG waves as compared to surface ECG due to site-typical electrode motion artifacts. False positive bradycardia as indicated with more than 13 consecutive FN ECG waves was equally expectable in esophageal and surface channels. No adverse events were reported for the multi-electrode gastric feeding tube. Conclusion: Heart rate monitoring of preterm infants with multiple esophageal electrodes is considered as feasible and reliable. Less signal artifacts will improve the detection of bradycardia, which is crucial for immediate interventions, and reduce alarm fatigue. Significance: Due to the possibility to integrate the multichannel ECG into a gastric feeding tube and meanwhile omit harmful skin electrodes, the presented system has great potential to facilitate future intensive care of preterm infants. [ABSTRACT FROM AUTHOR]

In this note, a node-augment mechanism is proposed to handle the problem of privacy preservation for a leader-following multi-agent system, where for each real node (agent) of the multi-agent system an associated virtual node is introduced. The real node and the associated virtual node form a directed node pair with the state information of virtual node sent to the real node, and information exchanges occur among virtual nodes of neighboring node pairs. It is proved rigorously that both the leader-following consensus and privacy preservation of real nodes’ state are achieved by appropriately designing control protocol. Finally, numerical simulation shows the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

We report on alpha and neutron irradiation of 7.8 $\boldsymbol {\mu }\text{m}$ single-photon avalanche diodes (SPADs) manufactured in 40-nm CMOS. Displacement damage leads to persistent dark count rate hot spots, due to carrier generation centers introduced by radiation. Defects are studied as a function of temperature and electrical stress, with live acquisitions evidencing large amounts of short-term annealing of the damage, and sometimes also random telegraph signal. Average damage factors and defect activation energies are consistent with a large field-enhancement of carrier generation. Since some radiation-induced defects lead to out-of-spec behavior, the implications for the reliability of SPAD-based systems are also discussed, by projecting the associated terrestrial failure rates. [ABSTRACT FROM AUTHOR]

Hierarchical state estimation (HSE) is often deployed to evaluate the states of an interconnected power system from telemetered measurements. By HSE, each low-level control center (LCC) takes charge of the estimation of its internal states, whereas a trusted high-level control center (HCC) assumes the coordination of boundary states. However, a trusted HCC may not always exist in practice; a cloud server can take the role of an HCC in case no such facility is available. Since it is prohibited to release sensitive power grid data to untrustworthy cloud environments, considerations need to be given to avoid breaches of LCCs’ privacy when outsourcing the coordination tasks to the cloud server. To this end, this article proposes a privacy-preserving HSE framework, which rearranges the regular HSE procedure to integrate a degree-2 variant of the Thresholded Paillier Cryptosystem (D2TPC). Attributed to D2TPC, computations by the cloud-based HCC can be conducted entirely in the ciphertext space. Even if the HCC and some LCCs conspire together to share the information they have, the privacy of non-conspiring LCCs is still assured. Experiments on various scales of test systems demonstrate a high level of accuracy, efficiency, and scalability of the proposed framework. [ABSTRACT FROM AUTHOR]

Vehicular local advertising enables roadside retailers to provide timely and location-aware advertisements to on-road vehicular users to attract more in-store visits. However, the prevalence of the vehicular local advertising has raised increasing transparency and accountability concerns on the spatial keyword query process, such as why a vehicular user receives advertisements from specific spatial entities as well as the timely detection of the advertising misbehavior. In this paper, we develop a transparent and accountable vehicular local advertising system by utilizing the tamper-proof and open nature of the blockchain technology. Considering the large scale of the spatial-keyword database and the expensive computation and storage cost on the blockchain, we introduce two design strategies, digest-and-verify and divide-then-assemble. In specific, a large-scale spatial keyword database is digested and stored on the blockchain. The spatial keyword query is then conducted with modular executions of two off-chain spatial keyword query functions, the results of which are efficiently assembled and verified in an advertising smart contract. By doing so, expensive on-chain computation and storage overheads are significantly reduced at a cost of acceptable off-chain overheads. We define the security requirements of the vehicular local advertising system as Auditing Security and achieve the notion with the security analysis. We also conduct real-world experiments to show the efficiency of the blockchain-based vehicular local advertising system. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) technology has been identified as one of the major digital innovations that has revolutionized not only the field of the industry but also the construction. From a research side, AM is a multidisciplinary domain, combining between materials science, mechatronics engineering, and architectural design. The AM concept needs to consider the geometry and the shape of printed objects regarding material properties and robot kinematics which are adaptable to the object size. In this article, we present an integrated design and control of an omnidirectional mobile manipulator robot (MMR), capable of extruding concrete and clay materials, to print complex and funicular architectural geometries. The studied robot allows printing building pieces with different shapes and sizes by additive deposit which can later be assembled on-site. The main issue concerns the control of continuous material deposit, with specified accuracy and respecting the desired shape. This problem involves to solve, in real time, the nonlinear kinematic model of a ten-degrees-of-freedom heterogeneous robot. The kinematic model and the control of utilized MMR are discussed, and an optimization method based on quadratic programming is used for handling the robot redundancy. Finally, we present the experimental results to illustrate the efficiency of AM on the developed concept. [ABSTRACT FROM AUTHOR]

A multitude of privacy-enhancing technologies (PETs) has been presented recently to solve the privacy problems of contemporary services utilizing cloud computing. Many of them are based on additively homomorphic encryption (AHE) that allows the computation of additions on encrypted data. The main technical obstacles for adaptation of PETs in practical systems are related to performance overheads compared with current privacy-violating alternatives. In this article, we present a hardware/software (HW/SW) codesign for programmable systems-on-chip (SoCs) that is designed for accelerating applications based on the Paillier encryption. Our implementation is a microcode-based multicore architecture that is suitable for accelerating various PETs using AHE with large integer modular arithmetic. We instantiate the implementation in a Xilinx Zynq-7000 programmable SoC and provide performance evaluations in real hardware. We also investigate its efficiency in a high-end Xilinx UltraScale+ programmable SoC. We evaluate the implementation with two target use cases that have relevance in PETs: privacy-preserving computation of squared Euclidean distances over encrypted data and multi-input functional encryption (FE) for inner products. Both of them represent the first hardware acceleration results for such operations, and in particular, the latter one is among the very first published implementation results of FE on any platform. [ABSTRACT FROM AUTHOR]

This article presents a portable and programmable frequency measurement system (PrO-FMS) with 0.1-ppm RMSE resolution over a measurement time interval of 33.2 s for bulk-acoustic resonator applications. PrO-FMS has a feature of choosing a frequency estimation method and accordingly the sampling frequency, which is programmable. Five frequency estimation methods, Candan, Djukanovic, Prony, M-Pisarenko, and zero crossing interpolation methods, are reviewed for high-resolution frequency measurement. PrO-FMS can also be reconfigured to select a particular resonance mode over a range of 1–10 MHz. Measurement results are provided for two different commercial quartz crystals and a microfabricated in-plane bulk acoustic resonator in two different modes of resonance. The results of these estimation methods are compared with a standard commercial tabletop frequency counter. Measurement results show that PrO-FMS can achieve a resolution of 0.1 ppm RMSE. For the same gate time, PrO-FMS provides better resolution than standard tabletop frequency counter. This article also provides a study on the behavior of the five different frequency estimation methods for short- and long-term measurements for quartz crystals and bulk acoustic resonator in two different modes. [ABSTRACT FROM AUTHOR]

We propose a privacy-preserving face authentication system for smartphones that guarantees security against malicious clients. Using the proposed system, a face feature vector is stored on a remote server in encrypted form. To guarantee security against an honest-but-curious server who may try to learn the private feature vector, we perform a Euclidean distance-based matching score computation on encrypted feature vectors using homomorphic encryption. To provide security against malicious clients, we adopt a blinding technique. We implement the proposed system on a mobile client and a desktop server. Through an experiment with real-world participants, we demonstrate that secure face verification can be completed in real time (within 1.3 s) even when a smartphone is involved, with an Equal Error Rate (EER) of 3.04%. In further experiments with two public face datasets, CFP and ORL, face verification is completed in approximately 1 s with EER of 1.17% and 0.37%, respectively. Our system is two orders of magnitude faster than previous privacy-preserving face verification method with the same security assumptions and functionalities. To achieve this secure real-time computation, we improve the Catalano-Fiore transformation which converts a linear homomorphic encryption scheme into a quadratic scheme, and parallelize the decryption procedure of our system. [ABSTRACT FROM AUTHOR]

As an emerging training model with neural networks, federated learning has received widespread attention due to its ability to update parameters without collecting users’ raw data. However, since adversaries can track and derive participants’ privacy from the shared gradients, federated learning is still exposed to various security and privacy threats. In this paper, we consider two major issues in the training process over deep neural networks (DNNs): 1) how to protect user’s privacy (i.e., local gradients) in the training process and 2) how to verify the integrity (or correctness) of the aggregated results returned from the server. To solve the above problems, several approaches focusing on secure or privacy-preserving federated learning have been proposed and applied in diverse scenarios. However, it is still an open problem enabling clients to verify whether the cloud server is operating correctly, while guaranteeing user’s privacy in the training process. In this paper, we propose VerifyNet, the first privacy-preserving and verifiable federated learning framework. In specific, we first propose a double-masking protocol to guarantee the confidentiality of users’ local gradients during the federated learning. Then, the cloud server is required to provide the Proof about the correctness of its aggregated results to each user. We claim that it is impossible that an adversary can deceive users by forging Proof, unless it can solve the NP-hard problem adopted in our model. In addition, VerifyNet is also supportive of users dropping out during the training process. The extensive experiments conducted on real-world data also demonstrate the practical performance of our proposed scheme. [ABSTRACT FROM AUTHOR]

Identifying the structure of cities has long been studied in urban planning and traffic modeling. This study presents a reliable method that reveals the structure of cities mainly based on clustering analysis using a new similarity measure. Several previous studies have used well-known clustering algorithms in machine learning fields, such as $k$ -means clustering based on temporal mobility patterns of regions, whereas other studies have applied community detection algorithms on networks that depict traffic flows. However, the former does not reflect spatial interactions among places or areas, and the latter groups’ places or areas with different land uses into the same cluster. To address these issues in existing approaches, this study proposes a new similarity method that considers not only temporal mobility patterns of areas but also spatial interactions with other areas. Moreover, the study combines spectral clustering repeated several times with hierarchical clustering to obtain a reliable structure that keeps the contiguity of clusters and determine the hierarchy of different areal units. The application of the proposed method to the data for Seoul, South Korea, reveals that the proposed clustering process divides a city into relatively homogeneous areas in terms of land uses. The flow maps based on the clustering results also revealed the spatial interactions between different areas and identify the polycentric structure of Seoul. [ABSTRACT FROM AUTHOR]

Silicon (Si) single-photon avalanche diode (SPAD) arrays sensitive to the 400- to 900-nm wavelength range have been studied for a number of uses due to their high detection efficiency; zero readout noise; low timing jitter; mechanical robustness; low-voltage (< 50 V) operation; mass producibility; and low size, weight, power, and cost. As with all solid-state detectors, however, they are susceptible to damage by radiation, with displacement damage by energetic particles being the primary concern for many applications. This article reports the results of low-dose-rate neutron irradiation from an americium–beryllium (AmBe) source on a 256×256 array of 10-μm active diameter SPADs, each with live recording of their dark count rate (DCR), up to a cumulative displacement damage dose equivalent to that delivered by a 1-MeV neutron fluence of 5.69 ⋅ 109 cm−2 in Si (11.59 TeV/g). A small fraction (~2.57%) of the SPADs in the array experienced a significant step increase in their DCR, which we ascribe to displacement damage. The remainder experienced a far more gradual and subdued increase. For this reason, an operational concept for SPAD arrays, whereby SPADs that begin to exhibit a step increase in DCR are disabled, should significantly extend the useful performance of such arrays in radiation environments. We report the surviving proportion of an array implementing such an operational concept per unit dose and its DCR. [ABSTRACT FROM AUTHOR]

Constant modulus algorithm (CMA) has been widely used in direction of arrival (DOA) estimation for constant modulus signals. However, CMA fails completely when dealing with array elements failure. In this paper we devise an algorithm to solve CMA fault-tolerance deficiency. The compensation of failed elements in 2D arrays is achieved by a Replace, Replicate, Reconstruct and Remove (4R) algorithm. The 4R-2D-CMA starts by replacing a failed-element signal by the nearest operating array element signal, then, after computing the covariance matrix, the corrupted rows and columns corresponding to failed elements in both elevation and azimuth are replicated from their Centro-symmetric counterparts to correct the covariance distortion. Replication step takes advantage of a special structure of two perpendicular Centro-symmetric arrays producing a Centro-Hermitian matrix. Afterwards, a 2D-CMA algorithm is used for decomposition to produce azimuth and elevation directions matrices. After decomposition, a linear phase correction is used before the covariance matrix is reconstructed. Finally, a second decomposition is used to obtain final DOAs. The number of constant modulus (CM) sources is estimated from a robust spectrogram analysis. Results for an SNR of −3 dB with 11% of elements failure show almost full recovery and hence prove the effectiveness of the novel approach. [ABSTRACT FROM AUTHOR]

The ability to handle outliers is essential for performing the perspective- $n$ n -point (P $n$ n P) approach in practical applications, but conventional RANSAC+P3P or P4P methods have high time complexities. We propose a fast P $n$ n P solution named R1PP $n$ n P to handle outliers by utilizing a soft re-weighting mechanism and the 1-point RANSAC scheme. We first present a P $n$ n P algorithm, which serves as the core of R1PP $n$ n P, for solving the P $n$ n P problem in outlier-free situations. The core algorithm is an optimal process minimizing an objective function conducted with a random control point. Then, to reduce the impact of outliers, we propose a reprojection error-based re-weighting method and integrate it into the core algorithm. Finally, we employ the 1-point RANSAC scheme to try different control points. Experiments with synthetic and real-world data demonstrate that R1PP $n$ n P is faster than RANSAC+P3P or P4P methods especially when the percentage of outliers is large, and is accurate. Besides, comparisons with outlier-free synthetic data show that R1PP $n$ n P is among the most accurate and fast P $n$ n P solutions, which usually serve as the final refinement step of RANSAC+P3P or P4P. Compared with REPP $n$ n P, which is the state-of-the-art P $n$ n P algorithm with an explicit outliers-handling mechanism, R1PP $n$ n P is slower but does not suffer from the percentage of outliers limitation as REPP $n$ n P. [ABSTRACT FROM AUTHOR]

This article presents a portable and programmable frequency measurement system (PrO-FMS) with 0.1-ppm RMSE resolution over a measurement time interval of 33.2 s for bulk-acoustic resonator applications. PrO-FMS has a feature of choosing a frequency estimation method and accordingly the sampling frequency, which is programmable. Five frequency estimation methods, Candan, Djukanovic, Prony, M-Pisarenko, and zero crossing interpolation methods, are reviewed for high-resolution frequency measurement. PrO-FMS can also be reconfigured to select a particular resonance mode over a range of 1–10 MHz. Measurement results are provided for two different commercial quartz crystals and a microfabricated in-plane bulk acoustic resonator in two different modes of resonance. The results of these estimation methods are compared with a standard commercial tabletop frequency counter. Measurement results show that PrO-FMS can achieve a resolution of 0.1 ppm RMSE. For the same gate time, PrO-FMS provides better resolution than standard tabletop frequency counter. This article also provides a study on the behavior of the five different frequency estimation methods for short- and long-term measurements for quartz crystals and bulk acoustic resonator in two different modes. [ABSTRACT FROM AUTHOR]

Industrial communication systems represent one of the most important innovations of the last decades in the context of factory and process automation systems. They are networks specifically designed to cope with the tight requirements of these challenging application fields such as real time, determinism, and reliability. Moreover, industrial networks are often deployed in environments characterized by strong electromagnetic interference, mechanical stress, critical temperature, and humidity. Over the last three decades, different classes of industrial networks have been developed according to changing requirements and available communication and information technologies. In this paper, we first provide an account of the state of the art, reviewing classical fieldbuses, real-time Ethernet networks, and industrial wireless networks, along with their most relevant features, applications, and performance figures. We introduce the complex standardization framework and analyze the market status and assumptions for future development. In the second part, we address the future perspectives focusing on new technologies, standards, and fields of application. In particular, we consider the time-sensitive networking (TSN) family of standards, Industrial Internet-of-Things (IIoT) systems, high-performance wireless LANs, industrial applications of cellular networks, and Ethernet networks for automotive communication. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of $\mathcal {L}_{2}$ – $\mathcal {L}_{\infty }$ dynamic output feedback controller (DOFC) design for nonlinear switched systems with nonlinear perturbations in the Takagi–Sugeno fuzzy framework. First, the average dwell time approach is used to stabilize a nonlinear switched system exponentially under an arbitrary switching law. Then, based on the technique of piecewise Lyapunov functions, a fuzzy-rule-dependent DOFC is designed to ensure that the overall closed-loop system is exponentially stable with a weighted $\mathcal {L}_{2}$ – $\mathcal {L}_{\infty }$ performance level $\left(\gamma,\alpha \right)$. The solvability condition for the desired DOFC is derived using a linearization technique. It is shown that the controller parameters can be obtained as solutions to a set of strict linear matrix inequalities that are numerically solvable with available standard software. Finally, two simulation examples illustrate effectiveness of the developed technique, including cognitive-radio systems. [ABSTRACT FROM AUTHOR]

With more organizational and individual data owners storing their data in the cloud, there is a corresponding need to ensure that such outsourced data can be computed by the data owners or some authorized third parties. In other words, the cloud server performs the requested computation, and returns the computation result to the requesting party. Such a paradigm is referred to as outsourced computation in the literature. A challenge is how one can ensure the correctness of the returned result, and this has been extensively studied in the literature. For example, in 2017, Song et al. proposed a protocol for publicly verifiable computation of polynomials over outsourced data with multiple sources. However, we reveal that a core building block of their protocol allows an adversary to forge the signatures on the outsourced data. Hence, this invalidates the security of the protocol. We also point out their proposal is yet very interesting and useful, only a little improvement can strengthen their proposal to be secure. [ABSTRACT FROM AUTHOR]

This brief presents a two-path discrete-time third-order sigma-delta ($\Sigma \Delta$) modulator with an extra zero in the noise transfer function (NTF) located at $z = -1$ , reducing the NTF coefficients of intermediate terms for optimal design. Applying polyphase decomposition of the NTF, the proposed $\Sigma \Delta$ modulator is implemented by a two-path architecture with cross-coupling branches. The 65-nm CMOS experimental chip running at a sampling rate of 340 MHz achieves a DR of 68.8 dB and a SNDR of 65.4 dB for a 10-MHz signal bandwidth, occupying an active area of 0.2257 mm2 and consuming 19.47 mW from a 1.2-V supply. [ABSTRACT FROM AUTHOR]

Establishing promising and efficient key distribution is the cornerstone of security when applying cryptographic methods to guard the privacy of smart devices in the Internet of Things (IoT). However, when using conventional key distribution methods in real-time mobile services, it is usually deficient to build multiple channels with strong security simultaneously on a single data server. Therefore, we focus our research on collecting or monitoring real-time and remote data, which is commonly observed in the health care field of the IoT. In this research, there are difficulties to apply the conventional key distribution approaches during interactions when considering both security and efficiency. Moreover, when preserving personal privacy, anonymity is also important in key distribution processes. We design a system of real-time mobile services and the related security requirements in this paper, and apply a novel and interesting Identity-Based Key Encapsulation Mechanism (IBKEM) to instantiate our system and achieve anonymous key distribution with only one-pass communication for mobile clients in batch. Our instantiated system demonstrates both strong security and practice. [ABSTRACT FROM AUTHOR]

Cloud computing has arisen as the mainstream platform of utility computing paradigm that offers reliable and robust infrastructure for storing data remotely, and provides on demand applications and services. Currently, establishments that produce huge volume of sensitive data, leverage data outsourcing to reduce the burden of local data storage and maintenance. The outsourced data, however, in the cloud are not always trustworthy because of the inadequacy of physical control over the data for data owners. To better streamline this issue, scientists have now focused on relieving the security threats by designing remote data checking (RDC) techniques. However, the majority of these techniques are inapplicable to big data storage due to incurring huge computation cost on the user and cloud sides. Such schemes in existence suffer from data dynamicity problem from two sides. First, they are only applicable for static archive data and are not subject to audit the dynamic outsourced data. Second, although, some of the existence methods are able to support dynamic data update, increasing the number of update operations impose high computation and communication cost on the auditor due to maintenance of data structure, i.e., merkle hash tree. This paper presents an efficient RDC method on the basis of algebraic properties of the outsourced files in cloud computing, which inflicts the least computation and communication cost. The main contribution of this paper is to present a new data structure, called Divide and Conquer Table (D&CT), which proficiently supports dynamic data for normal file sizes. Moreover, this data structure empowers our method to be applicable for large-scale data storage with minimum computation cost. The one-way analysis of variance shows that there are significant differences between the proposed method and the existing methods in terms of the computation and communication cost on the auditor and cloud. [ABSTRACT FROM AUTHOR]

Somewhat Homomorphic Encryption (SHE) schemes allow to carry out operations on data in the cipher domain. In a cloud computing scenario, personal information can be processed secretly, inferring a high level of confidentiality. For many years, practical parameters of SHE schemes were overestimated, leading to only consider the FFT algorithm to accelerate SHE in hardware. Nevertheless, recent work demonstrates that parameters can be lowered without compromising the security [1] . Following this trend, this work investigates the benefits of using Karatsuba algorithm instead of FFT for the Fan-Vercauteren (FV) Homomorphic Encryption scheme. The proposed accelerator relies on an hardware/software co-design approach, and is designed to perform fast arithmetic operations on degree 2,560 polynomials with 135 bits coefficients, allowing to compute small algorithms homomorphically. Compared to a functionally equivalent design using FFT, our accelerator performs an homomorphic multiplication in 11.9 ms instead of 15.46 ms, and halves the size of logic utilization and registers on the FPGA. [ABSTRACT FROM AUTHOR]

Although data confidentiality is the primary security objective in additive encrypted computation applications, such as the aggregation of encrypted votes in electronic elections, ensuring the trustworthiness of data is equally important. And yet, integrity protections are generally orthogonal to additive homomorphic encryption, which enables efficient encrypted computation, due to the inherent malleability of homomorphic ciphertexts. Since additive homomorphic schemes are founded on modular arithmetic, our framework extends residue numbering to support fast modular reductions and homomorphic syndromes for detecting random errors inside homomorphic ALUs and data memories. In addition, our methodology detects malicious modifications of memory data, using keyed syndromes and block cipher-based integrity trees, which allow preserving the homomorphism of ALU operations, while enforcing non-malleability of memory data. Compared to traditional memory integrity protections, our tree-based syndrome generation and updating is parallelizable for increased efficiency, while requiring a small Trusted Computing Base for secret key storage and block cipher operations. Our evaluation shows more than 99.999 percent detection rate for random ALUs errors, as well as 100 percent detection rate of single bit-flips and clustered multiple bit upsets, for a runtime overhead between 1.2 and 5.5 percent, and a small area penalty. [ABSTRACT FROM PUBLISHER]

Dynamic Proof of Storage (PoS) is a useful cryptographic primitive that enables a user to check the integrity of outsourced files and to efficiently update the files in a cloud server. Although researchers have proposed many dynamic PoS schemes in single-user environments, the problem in multi-user environments has not been investigated sufficiently. A practical multi-user cloud storage system needs the secure client-side cross-user deduplication technique, which allows a user to skip the uploading process and obtain the ownership of the files immediately, when other owners of the same files have uploaded them to the cloud server. To the best of our knowledge, none of the existing dynamic PoSs can support this technique. In this paper, we introduce the concept of deduplicatable dynamic proof of storage and propose an efficient construction called DeyPoS, to achieve dynamic PoS and secure cross-user deduplication, simultaneously. Considering the challenges of structure diversity and private tag generation, we exploit a novel tool called Homomorphic Authenticated Tree (HAT). We prove the security of our construction, and the theoretical analysis and experimental results show that our construction is efficient in practice. [ABSTRACT FROM PUBLISHER]

Systems employing biometric traits for people authentication and identification are witnessing growing popularity due to the unique and indissoluble link between any individual and his/her biometric characters. For this reason, biometric templates are increasingly used for border monitoring, access control, membership verification, and so on. When employed to replace passwords, biometrics have the added advantage that they do not need to be memorized and are relatively hard to steal. Nonetheless, unlike conventional security mechanisms such as passwords, biometric data are inherent parts of a person?s body and cannot be replaced if they are compromised. Even worse, compromised biometric data can be used to have access to sensitive information and to impersonate the victim for malicious purposes. For the same reason, biometric leakage in a given system can seriously jeopardize the security of other systems based on the same biometrics. A further problem associated with the use of biometric traits is that, due to their uniqueness, the privacy of their owner is put at risk. Geographical position, movements, habits, and even personal beliefs can be tracked by observing when and where the biometric traits of an individual are used to identify him/her. [ABSTRACT FROM PUBLISHER]

Cloud-assisted vehicular delay tolerant networks (DTNs) have been utilized in wide-ranging applications where a continuous end-to-end connection is unavailable, the message transmission is fulfilled by the cooperation among vehicular nodes and follows a store-carry-and-forward manner, and the complex computational work can be delegated to the disengaged vehicles in the parking lots which constitute the potential vehicular cloud. Nevertheless, the existing incentive schemes as well as the packet forwarding protocols cannot well model continuous vehicle collaboration, resist vehicle compromise attacks and collusion attacks, leaving the privacy preservation issues untouched. In this paper, a novel threshold credit-based incentive mechanism (TCBI) is proposed based on the modified model of population dynamics to efficiently resist the node compromise attacks, stimulate the cooperation among intermediate nodes, maximize vehicular nodes’ interest, and realize the fairness of possessing the same opportunity of transmitting packets for credits. Then, a TCBI-based privacy-preserving packet forwarding protocol is proposed to solve the open problem of resisting layer-adding attack by outsourcing the privacy-preserving aggregated transmission evidence generation for multiple resource-constrained vehicles to the cloud side from performing any one-way trapdoor function only once. The vehicle privacy is well protected from both the cloud and transportation manager. Finally, formal security proof and the extensive simulation show the effectiveness of our proposed TCBI in resisting the sophisticated attacks and the efficiency in terms of high reliability, high delivery ratio, and low average delay in cloud-assisted vehicular DTNs. [ABSTRACT FROM PUBLISHER]

Bit-width allocation has a crucial impact on hardware efficiency and accuracy of fixed-point arithmetic circuits. This paper introduces a new accuracy-guaranteed word-length optimization approach for feed-forward fixed-point designs. This method uses affine arithmetic, which is a well-known analytical technique, for both range and precision analyses. This paper introduces an acceleration technique and two new semianalytical algorithms for precision analysis. While the first algorithm follows a progressive search strategy, the second one uses a tree-shaped search method for fractional width optimization. The algorithms offer two different time-complexity/cost efficiency tradeoffs. The first algorithm has polynomial complexity and achieves comparable results with existing heuristic approaches. The second algorithm has exponential complexity, but it achieves near-optimal results compared to the exhaustive search method. A commonly used set of case studies is used to evaluate the efficiency of the proposed techniques and algorithms in terms of optimization time and hardware cost. The first and second algorithms achieve 10.9% and 13.1% improvements in area, respectively, over uniform fractional width allocation. The proposed acceleration technique reduces the complexity of the fractional width selection problem by an average of 20.3%. [ABSTRACT FROM PUBLISHER]

We propose a novel approach for the estimation of the pose and focal length of a camera from a set of 3D-to-2D point correspondences. Our method compares favorably to competing approaches in that it is both more accurate than existing closed form solutions, as well as faster and also more accurate than iterative ones. Our approach is inspired on the EPnP algorithm, a recent O(n) solution for the calibrated case. Yet we show that considering the focal length as an additional unknown renders the linearization and relinearization techniques of the original approach no longer valid, especially with large amounts of noise. We present new methodologies to circumvent this limitation termed exhaustive linearization and exhaustive relinearization which perform a systematic exploration of the solution space in closed form. The method is evaluated on both real and synthetic data, and our results show that besides producing precise focal length estimation, the retrieved camera pose is almost as accurate as the one computed using the EPnP, which assumes a calibrated camera. [ABSTRACT FROM AUTHOR]