We present Mask, an efficient and tunable algorithm for hiding volume patterns in multi-maps. Volume-pattern leakage, referring to the observable size of data returned by a query, enables adversaries to infer sensitive dataset information, posing severe privacy risks in multi-map scenarios. Designed to address this issue, Mask focuses on balancing storage/query overhead and privacy (a key trade-off in this field), allowing users to define parameters for random data distribution across buckets to realize fine-grained control over storage and query performance, integrating Bloom filters with a bounded cache for efficient indexing (optimizing performance under skewed query workloads); extended to MaskIO for reduced client-side storage, it obfuscates query indexes and uploads them to the server, achieving constant-bounded client storage overhead, and experiments show Mask outperforms the bucket-based peer Veil with 2–4 × higher performance and a lower stash ratio.

The massive growth of data has brought vigorous vitality to Internet-of-Things (IoT). It has also brought new challenges, such as confidentiality privacy protection and redundant data transmission. Concerning this regard, data aggregation serves as an efficient technique to minimize the transmission frequency among massive objects in smart grid(SG). By aggregating a large amount of the same type of data while satisfying the protection of user privacy. With the advent of the post-quantum era, a good aggregation scheme must provide quantum resistance while ensuring the secure aggregation of ciphertext power data. However, the excessive overhead limits anti-quantum algorithms from being widely used in SG where resource devices are limited. Therefore, it is an important part of the current private data security aggregation technology to find a low cost and lightweight inverse quantum algorithm to achieve user data security aggregation. In this paper, we propose an improved NTRU-based cryptosystem with multidimensional coding, referred to as multidimensional coding NTRU (MC-NTRU). and use the lattice batch signature technique, which improves the efficiency of the scheme while satisfying the anti-quantum attack. Based on these, we design the multidimensional auditable lattice-based privacy-preserving data aggregation scheme(MA-PPDA) for privacy data on resource-limited IoT devices such as smart grids. In addition to this, the scheme achieves fault tolerance of the scheme by adding zeros and random numbers to the user data. The comparative study against existing approaches demonstrates that the proposed scheme not only adheres to critical security aspects including user privacy, data confidentiality, integrity, and authenticity, but also decreases both communication and computational burdens on the system. This makes our scheme particularly apt for IoT environments characterized by constrained device resources.

Federated Learning (FL) enables collaborative model training while preserving data privacy, making it particularly attractive for large-scale Internet of Things (IoT) systems. However, in practical deployments, data collected by distributed clients are often non-independent and identically distributed (Non-IID), which amplifies the vulnerability of FL to poisoning attacks. Among them, Label-Flipping Attacks (LFA) are especially stealthy, as they can induce targeted misclassification without noticeably affecting overall accuracy, posing serious risks to safety-critical IoT applications. In this paper, we propose NeuDFL, a lightweight and robust defense framework against LFA under Non-IID settings. Unlike many existing defenses that primarily rely on gradient analysis over the full model update space or auxiliary clean datasets, NeuDFL exploits lightweight class-wise parameter statistics extracted from the final fully connected layer. By leveraging the cumulative and task-aligned nature of model parameters, NeuDFL enables reliable identification of attacked classes and filters malicious clients via an adaptive statistical threshold, improving robustness to data heterogeneity while incurring low computational overhead. Extensive experiments on multiple datasets demonstrate that NeuDFL offers an effective and efficient defense against label-flipping attacks, providing a robust solution for federated learning in complex real-world environments.

To capture the small signal of smart sensor system, the high-precision sigma delta analog to digital converter (ΣΔ ADC) is the essential components. Based on the reinforcement learning (RL) algorithm, a high-efficiency design method for 16-bit ΣΔ ADC is proposed in this research. The key circuit modules in the ΣΔ ADC are extracted to be separately optimized to decrease the design complexity. The prior knowledge of circuits can be introduced into the search process of RL algorithm to accelerate the search speed. In addition, the deep neural network (DNN) models are trained to replace the circuit simulator to reduce the cost of simulation resource. Two key circuits in the ΣΔ ADC are optimized. Compared with the advanced method, the proposed optimization method has the fastest convergency and best performance, which demonstrates the effectiveness of the proposed method. In addition, the simulation time can be decreased by 98.3 %, which can greatly speed up the optimization process. The optimized ΣΔ ADC is taped out. The test results show that the effective resolution is 16.16 bit, and the voltage noise is 8.7e-05 V. Compared with the original chip, the effective resolution can be improved by 6.3%, and the noise voltage can be decreased by 47%. Therefore, the proposed optimization design method for ΣΔ ADC can greatly decrease the design cycle and improve the performance of ΣΔ ADC, which presents the enormous potential application in the internet of things.

Vertical Federated Learning (VFL) is a distributed machine learning paradigm designed for scenarios with vertically partitioned data features, making it highly compatible with Internet of Things (IoT) ecosystems. While promoting collaborative modeling among IoT devices, VFL also introduces new security risks, particularly backdoor attacks. Existing VFL backdoor attacks typically establish associations between triggers and target labels during the training phase by manipulating intermediate model outputs, making them easily detectable by advanced defense mechanisms. This paper proposes Hijacking-based Backdoor Attack (HBA), which for the first time innovatively achieves backdoor attack by exchanging the forward embeddings during VFL prediction phase, without embedding traditional triggers. HBA leverages intrinsic semantic relationships in the embedding space to hijack the decision-making process of the top model during inference. HBA’s effectiveness depends on the discriminative nature of the features extracted by the bottom model, and since it does not alter the training process, it can evade most defense mechanisms based on training behavior monitoring. Experiments demonstrate that HBA achieves an attack success rate of 99.9% in classification tasks without compromising the original task’s accuracy. Furthermore, existing defense mechanisms struggle to effectively counter HBA without degrading the model’s original task performance.

Online social networks (OSN) such as Twitter, Facebook, etc. have an overall user base of more than 5 billion as of today. Traditional centralized OSN users’ data and content are stored in centralized servers, which has the risk of data leakage and privacy violation. Distributed OSN (DOSN) address the single-point-of-failure and user data privacy concerns faced by centralized OSN by enabling the operation of network infrastructures and services without centralized ownership or control. However, DOSN face privacy protection issues. The group key agreement (GKA) is an important method to construct secure channels to protect the secure communication of network group members. Asymmetric GKA methods allow external members to securely communicate with group members without having to join the group. However, it is worth noting that existing AGKA schemes rely on bilinear pairing, resulting in a high computational overhead. Meanwhile, considering scenarios where external users join, or group members leave, we design a dynamic asymmetric group key agreement (DAGKAwP) scheme based on Schnorr batch multi-signature that does not depend on bilinear pairing. During the key generation phase, the members generate self-authenticating public-private key pairs to resist malicious public key attacks. For group key agreement, new hash functions are embedded in Schnorr signatures to generate aggregated public keys, and this scheme supports external member addition and internal member exit. In group message encryption, sender anonymity and message non-repudiation are realized. The security comparison with related DAGKA schemes reveals that the DAGKAwP scheme offers more comprehensive security. Performance evaluations suggest that this scheme offers computational efficiency and lower communication overhead than related Dynamic AGKA schemes.

Reliability degradation caused by overheating hotspots and signal integrity issues is a critical challenge in ultrawideband (UWB) system, and the excellent chip floorplan design can significantly minimize such risks. In this research, an efficient floorplan optimization design method is developed utilizing a constrained particle swarm optimization with linear decreasing inertia weight (PSO-LDIW) algorithm. The finite element model of UWB system is established based on COMSOL Multiphysics software. In addition, the multiobjective function is constructed to minimize the peak temperature and interconnection distance of the UWB system. A global search is performed by the PSO-LDIW algorithm within a constrained design space to efficiently identify the minimum value of the objective function, which can determine the optimal floorplan coordinates of chips in UWB system. The effectiveness of the developed efficient floorplan optimization design method is verified by finite element simulation and experimental results. The maximum relative errors of peak temperature and interconnection distance between simulated and tested results are 1.55% and 0.08%, respectively. Therefore, the thermal reliability and interconnection distance of UWB system can be controlled by the developed efficient floorplan optimization design method.

The design for the analog-to-digital converter (ADC) system relies heavily on manual experience, and the performance is sensitive to variations in the external environment. In this research, a high-efficiency optimization design method via transfer learning (TL) is proposed. High-accuracy convolutional neural network (CNN)-IC models are trained to describe the relationship between design parameters and performance metrics. Based on the extracted circuit features by CNN-IC models, a transfer scheme is proposed to establish high-accuracy CNN-IC models under different process, voltage, and temperature (PVT) conditions with few-shot samples. The fitting accuracy (R${}^{2}$) of all the transferred models can reach over 99%, and the required amount of data is 10% of the original data. An optimization function is established, which contains the performance metrics and PVT design specifications. Then, the constrained particle swarm optimization (PSO) algorithm is adopted to efficiently optimize the design parameters considering multiple PVT conditions. Three key circuits in the ADC system, including a two-stage Miller-compensated operational amplifier (TSMCOA), bandgap reference (BGR), and successive approximation register (SAR) ADC circuits, are selected to be optimized. The optimized running times of the above three circuits are only 215, 341, and 549 s, respectively. The optimized power consumptions (PCs) are$56.22~\mu $W,$159~\mu $W, and 1.45 mW, respectively, while all performance metrics can meet the design specifications under different PVT conditions. Compared with the advanced model-based methods, the training data can be decreased by at least$2.3\times $,$3\times $, and$3\times $, respectively. Thus, the proposed method can effectively decrease the simulation cost and accelerate the design process.

This paper proposes a 1.5 GS/s 9-bit two-stage pipelined SAR analog-to-digital converter (ADC) in 28 nm CMOS. Simultaneously with amplification, the inter-stage residue is converted to time-domain (TD) and quantized by a TDC. This results in significant resolution improvement of the second stage under the speed of 1.5 GS/s. A double-edge quantized TDC with improved time resolution is proposed for higher conversion speed in the time domain and less power consumption. Moreover, a cascode switching inverter-based open-loop amplifier is proposed featuring low gain error, fast settling, and low power. With a sampling speed of 1.5 GS/s, the simulation results show that the ADC achieves 55 dB SNDR and 66.7 dB SFDR at the Nyquist frequency and 1-Vppinput swing, while the power consumption is 6.86 mW, yielding a Walden FoM of 10.2 fJ/con-step.

Chiplet interconnect technology meets the need for high performance, low power consumption and miniaturization in internet of things (IoT) devices. To address the co-design challenge of signal integrity and thermal stress reliability within chiplet interconnect structures, an intelligent optimization design method of ground signal-through silicon via (GS-TSV) with redistribution layers (RDLs) is developed. First, the least squares-support vector machine model is employed to map the relationship between geometrical parameters and performance metrics. Then, construct a multi-objective optimization function, and the linearly decreasing inertia weight-particle swarm optimization algorithm is applied to co-optimize. Finally, three cases are conducted to verify the feasibility and accuracy of the developed method using finite element software. The optimized return loss, insertion loss, peak temperature, and peak stress well agree with the specified targets, with the maximum relative errors of 2.75%, 1.07% and 2.76% for Case A, B and C, respectively. Eye diagram analysis shows that the optimized structure can support high speed data transmission up to 10 Gbps, and thermal stress reliability between the array GS-TSV with RDLs and the chiplet is analyzed. Therefore, the developed method effectively optimizes the design of interconnect structures in Chiplet-based integrated system, which can promote IoT development.

Voting protocols are fundamental in modern society. With the ongoing evolution of communication technology and the Internet of Things, the importance of electronic voting protocols is anticipated to experience a significant rise in the advancement of smart cities. Leveraging the blockchain’s tamper-resistant and publicly verifiable properties, along with the concept of enabling all participants to tally election results, blockchain-based self-tallying voting protocols effectively address the shortcomings of centralized traditional electronic voting. However, existing voting protocols still face the dual challenge of security and efficiency. The primary issues include the difficulty in ensuring voter anonymity and election fairness, as well as the insufficient system robustness and low tallying efficiency resulting from security design. To tackle these concerns, we propose a novel approach to constructing efficient and flexible voting protocols that concurrently ensure fairness and anonymity. Specifically, we encapsulate the decryption private keys corresponding to the public keys of encrypted ballots in time capsules to safeguard voting fairness. Additionally, based on our proposed approach, we construct an efficient and flexible score voting protocol for smart cities. The protocol employs traceable ring signature to protect the voter anonymity and public traceability, utilizes a dual-key additive homomorphic ElGamal encryption to encapsulate ballots. We also improve signature-based set membership proofs to verify the validity of ballots. Furthermore, through security analysis and performance evaluation, we demonstrate that our proposed protocol meets all security objectives with reasonable costs.

To improve the performance and decrease the design cost, a high-efficiency lightweight optimization method for analog-to-digital conversion (ADC) system in Internet of Things (IoT) is proposed in this paper. According to the circuit structure and the proposed design specifications, the circuit is divided into several sub-module circuits. Then, the two-stage lightweight circuit model is trained based on local convolutional neural network (CNN) and the global -multilayer perceptron (MLP) to predict the circuit performance metrics, which can replace the circuit simulator to decrease simulation time. Based on the established models, the parameters of analog circuit are optimized by the constrained particle swarm optimization (PSO) algorithm. Three typical circuits in ADC system are optimized and verified on Cadence software. The fitting accuracy (R2) of the established models achieves 99%. Compared with advanced model-based optimization method, the dataset and simulation time can be decreased 91% and 95%, and the optimized power consumption can be decreased 17%. Meanwhile, compared with the advanced reinforcement learning (RL)-based optimization method, the optimization time can be decreased 89.6%, and the optimized power consumption can be decreased 29.5% under the same dataset. The results show that the proposed lightweight optimization method can greatly decrease the dataset and simulation time, and improve the optimization efficiency of ADC system in IoT.

Ciphertext retrieval technology has been widely explored with the increasing popularity of cloud computing. Proxy re-encryption with Keyword search (PREKS) can support multi-user retrieval without increasing data owner's overhead, but users can continue searching once they have obtained search rights. It is difficult for a data owner to revoke a user's access rights because the data is stored in the cloud. In general, under the premise of forward and backward security, the user's search permission can be revoked by updating the ciphertext. Nevertheless, this approach may expose user or data privacy to cloud servers. In this paper, we utilize the Chinese Remainder Theorem to generate DO-Authorization and DU-Authentication factors, and realizes the authorization and revocation of a specific single user by adding or deleting authorization items. In addition, we use a designated tester algorithm in the ciphertext retrieval process to improve system security. Subsequent security analysis proves that the proposed scheme can resist the Chosen Keyword Attack and Keyword Guessing Attack. Simulation results indicate that the proposed scheme has high efficiency, especially in the user revocation phase

Electronic Health Record (EHR) sharing can improve the accuracy of medical diagnosis and promote the development of the public medical field. However, plaintext medical records expose patient privacy, and it is challenging to share medical records between different hospitals, making it difficult for doctors to securely retrieve medical records and massively analyze the same diseases from different patients. In this paper, we propose a flexible ciphertext retrieval scheme in electronic health sharing system that supports multi-patient and multi-keyword medical records retrieval. The proxy free ciphertext retrieval technology based on cloud servers enables doctors to retrieve multiple patients' medical records simultaneously. On this basis, we utilize the time encoding algorithm to ensure that the trapdoor associated with the old time cannot retrieve the ciphertext index bound with the latest time, so as to realize the lightweight forward security. Besides, the encoding time signed by the deniable signature can avoid forgery by malicious users or adversaries. In addition, the scheme supports evil user traceable and revocable. The security analysis indicates that the scheme can satisfy the keyword privacy, the forward security of updated ciphertexts, and the deniability of time encoding signatures. Experimental evaluation illustrates that our scheme is lightweight and efficient

In recent years, multi-dimensional range query (MRQ) over encrypted data has been increasingly applied in various scenarios, making it one of the mainstream services for secure large-scale data storage and sharing in cloud computing. However, although existing privacy-preserving MRQ schemes can ensure query and data privacy, they still fail to fully protect single-dimensional privacy and path pattern. Moreover, most schemes lack mechanisms to verify the completeness and correctness of query results, making it impossible to guarantee their validity. To address these issues, this paper proposes a secure and efficient MRQ scheme with result verification for multiple users (MU-MRQ). First, we design a secure and efficient multi-dimensional data index based on the G-tree, incorporating a timestamp mechanism to verify the correctness and completeness of query results. Additionally, we propose two novel intersection predicate encryption protocols to achieve efficient retrieval while preserving single-dimensional privacy and path pattern. Rigorous security analysis demonstrates that our MU-MRQ scheme achieves security under the known background model. Comprehensive experiments on real-world datasets validate the efficiency of the MU-MRQ scheme.

This article presents a single-channel 4-GS/s 8-bit hybrid-domain analog-to-digital converter (ADC) implemented in a 28-nm CMOS process. The proposed 8-bit ADC combines a 3-bit voltage-domain stage with a 6-bit time-domain (TD) backend to take full advantage of the voltage and time domains. A high-speed hybrid-loop residue amplifier (RA) is proposed with a settling time of less than 150 ps, while a non-symmetrical pipeline timing utilizing a 25% duty cycle clock is used to increase the TD quantization time and the settling time margin of the RA. A low-power and small-area gated-ring-oscillator-based TD backend is employed, which operates at 4-GS/s with 6-bit resolution. The prototype hybrid ADC occupies an active area of 0.0114 mm2. Under a 1-V power supply and Nyquist input, the chip achieves a measured ENOB of 6.46 bits at a conversion rate of 4 GS/s, while the power consumption is 10.6 mW and the FoMw is 29.9 fJ/conversion-step.

In recent years, cloud computing services have grown rapidly, with people outsourcing huge amounts of private data to cloud servers. Searchable encryption(SE) facilitates people’s use of data while protecting data privacy. To balance the efficiency, current SE schemes still leak information such as access, search and volume patterns to cloud servers, and most dynamic SE schemes leak forward and backward privacy, and these leaks create serious security threats. In this paper, we propose a dynamic SE scheme DMASP with suppressed leakage, which protects access pattern, search pattern and volume pattern simultaneously. We utilize the differential privacy mechanism to obfuscate databases, and introduce the CKKS algorithm to protect access and volume patterns during queries. Meanwhile, we design a secure structure to index databases efficiently, and protect forward and backward privacy during updates. We rigorously analyse the security of DMASP. Furthermore, comprehensive experimental evaluation show that DMASP can reduce the accuracy of attacks against patterns leakage in real-world databases.

To overcome the challenges of urbanization and population growth, smart cities use cutting-edge technologies, such as IoT and AI to offer improved public services. Although these advancements have brought convenience, they have raised security and privacy concerns. Blockchain technology has the potential to address these concerns, but existing blockchain frameworks have issues of scalability and efficiency that hinder their ability to meet the smart city demands. In this article, we propose a novel blockchain framework for smart cities, named SC-Chain. Specifically, SC-Chain incorporates a decentralized access mechanism that leverages threshold signatures and BFT-like consensus for efficient node registration and authentication in decentralized systems. Furthermore, we propose a consensus mechanism that utilizes the verifiable random function (VRF) to achieve efficient miner node election, ensuring efficiency, fairness, and security in large-scale smart city systems. We demonstrate the effectiveness and feasibility of the SC-Chain through theoretical analysis and simulations, showcasing its potential to enable the development of secure and efficient smart city infrastructure.

The attribute-based encryption (ABE) scheme, which can set specific conditions to control user access to data, has been widely studied and applied to cloud storage services. Considering file hierarchy in practical scenarios, the ABE scheme can set a hierarchical access control policy so multiple files can be associated with one access structure to reduce users’ computing overhead and save the cloud server’s storage space. However, the existing systems have the risk of user collusion due to the hierarchical access control structure parameters. This paper proposes a secure file hierarchy ABE scheme supporting user collusion resistance (CR-FH-CPABE) in cloud computing. We add a data noise vector without changing the hierarchical access control structure to prevent user ultra vires. Technically, we break the relationships that colluding users could exploit, prevent malicious users from colluding with their computing results, and extract meaningful information from the ciphertext. In addition, we provide an improved CR-FH-CPABE scheme with outsourced decryption, which helps resource-limited devices obtain computing services. Finally, we demonstrate our scheme is CPA secure and show outstanding performance through simulation results.

As a new machine learning (ML) paradigm, federated learning (FL) empowers different participants to jointly train a more effective model than traditional ML. Unlike horizontal FL (HFL), which expands the sample space by aggregating local models, vertical FL (VFL) is suitable for scenarios where the sample ID between participants is the same but differs in sample characteristics. For a long time, VFL has been considered safe due to no data exchange and heterogeneity between parties. However, the recently proposed label inference attacks pose a significant security threat to VFL. Specifically, by adding randomly initialized layers to the top of local models, the passive label inference attack can infer tens of thousands of local participants’ private data with only 40 auxiliary labels. Since the attack is entirely local, using privacy protection technologies such as differential privacy cannot effectively defend against these attacks. Therefore, we propose a new privacy protection scheme called FL similar gradients (FLSGs) to defend against this attack. Unlike differential privacy, the FLSG scheme randomly generates gradients of a Gaussian distribution similar in dimension to the original gradients and calculates their cosine distance. If the distance is less than a certain threshold, the gradients are used instead of the original gradients to pass to the local participants. We conducted extensive evaluations on six real-world data sets, and the results show that FLSG provides a better defensive effect at lower computational overhead than other known methods when defending the passive label inference attack.

The fine grain of ciphertext-policy attribute-based encryption (CP-ABE) offers advantages through the amalgamation of key and user attributes; however, it also brings the issue of key misuse. To circumvent the tracking mechanisms of the white-box algorithm, malicious users manipulate the decryption key and encryption algorithm, encapsulating them to create a black-box decryption device. This necessitates black-box traceability for supervision purposes. In this article, we employ$n$-bit encrypted binary vectors to depict the user’s identity and subsequently convert it into partial decryption privileges. When encrypting plaintext, data owners can utilize a “vague specification” to define the identity vector of qualified decryptors. Furthermore, based on the vague specification mechanism, we have devised a pioneering active black-box tracing algorithm. Integrating this algorithm with CP-ABE, we propose the black-box traceable CP-ABE (EIV-BT-ABE) scheme. Our EIV-BT-ABE scheme attains strong traceability with low time complexity, effectively reducing decryption and encryption time costs. The experiment substantiated the efficiency of our scheme while demonstrating its adherence to IND-CPA security.

The smart grid, which is an important component of smart cities, is developing rapidly nowadays, while the confidentiality and integrity of consumption data of customers become significant security issues. Although existing privacy-preserving aggregation schemes reduce the communication overhead and protect the privacy of users’ multidimensional power data, most of them are vulnerable and possess no quantum-resistant properties. In this article, we combine the Chinese remainder theorem (CRT) and the bit decomposition method to provide multibit homomorphic properties for the quantum-resistant algorithm number theory research unit (NTRU), and propose the partial-homomorphic NTRU (PH-NTRU). Then, based on the algorithm, we design the lattice-based multidimensional data privacy-preserving data aggregation scheme named FTCR-LMPPDA, in which the edge devices work as aggregator gateways. Specifically, smart meters participating in the aggregation process share zero-sum numbers to resist collusion attacks. Error retransmission mechanism and random number reconstruction algorithm are introduced to enhance the robustness of this scheme and provide our system with the ability to recover from faults. In addition, security analysis shows our scheme can resist quantum attacks, collusion attacks, and other internal and external attacks as well as keep the security features, such as confidentiality, privacy and integrity of users’ data. Finally, performance evaluation demonstrates that our scheme is more efficient than existing schemes and is more suitable for devices with constrained resources.

Ciphertext retrieval for cloud-based Internet of Things has been widely explored with the increasing popularity of cloud computing. However, in most existing solutions, the security and efficiency of the retrieval process are difficult to achieve simultaneously. To this end, we develop a novel secure matrix index, and we utilize identity-based encryption to encrypt keywords and homomorphic encryption to encrypt matrix values. The former can guarantee the security of the keyword, and the latter can realize the efficiency of the operation while ensuring safety. Then, we develop an identity-based bidirectional authentication algorithm to ensure that only authenticated users can retrieve ciphertext. In addition, we use different scoring formulas to calculate the relevance scores of documents with different lengths, ensuring that the documents are appropriately returned to users. Finally, we design a new retrieval structure to protect the privacy of the correspondence between keywords and ciphertexts. The security proof shows that the index and trapdoor can resist chosen keyword attack and keyword Guessing attack. The extensive simulation shows that our scheme is efficient.

Searchable encryption for Cloud-based Internet of Things has been widely explored with the increasing popularity of cloud computing. The public-key encryption with keyword search (PEKS) system support multiuser retrieval. However, the PEKS search time is linearly increasing as the index keyword number growth, and the search time would be huge if the index keywords consistently increase. In this article, we introduce a novel scheme named as public-key inverted-index keyword search with designated tester and multiuser key decryption (IDPEKS). First of all, we design an inverted index based on B-plus tree to reduce the search time to a logarithmic level. On this basis, we optimized the TF-IDF formula and added user preference factor and font size factor to make the relevance score calculation more consistent with needs of receiver. Besides, we design a multiuser key decryption algorithm to protect the system symmetric key. In addition, we set index server to perform the index-trapdoor retrieval process. The designated index server tester can resist the attack of the cloud server on keywords. The security proof shows that the scheme can resist the chosen keyword attack (CKA), keyword guessing attack (KGA), and key guessing attack (KeyGA). The experimental results show that the algorithm can improve retrieval efficiency while have a short encryption time.

In Industry 4.0, artificial intelligence (AI) has been successfully applied in scenarios, such as fault prediction, traffic analysis, and production decision making. However, due to the sensitivity and security of data, privacy regulations prohibit the transfer and exchange of industrial data between entities, resulting in training data being fragmented into data silos that limit the accuracy of AI models. FL can effectively break the data silo effect, but naive federated learning (FL) (FedAvg) is vulnerable to inference attacks from aggregators and Byzantine attacks from participants. To address these issues, we propose a privacy-preserving and Byzantine-robust federated learning scheme (PBFL) for Industry 4.0. Under the setting of an benign-majority participants, PBFL can always identify benign direction and magnitude of updates. Extensive experiments demonstrate that PBFL is more robust than state-of-the-art schemes, even with extreme proportion (49%) of malicious participants. Moreover, PBFL contains a series of well-optimized 2-party computation (2PC) protocols, causing it reduces total runtime of the unoptimized implementation by around$3 \times \sim 4 \times $and$9 \times \sim 10 \times $for 32-bit and 64-bit circuits, respectively.

In recent years, the Internet of Things (IoT) has gained immense popularity in various aspects of work, learning, and daily life. Within the IoT realm, there is a growing concern regarding communication security issues between users and servers. However, addressing the communication security between servers is equally imperative, which has not received as much attention. To this end, we propose a certificateless anonymous authenticated key agreement (AKA) algorithm based on learning with errors (LWEs) and inhomogeneous small integer solution (ISIS) security assumptions. Our scheme provides strong resistance to quantum attacks and protects the privacy of communication servers. It also has constant communication costs and lower computational requirements than existing lattice-based anonymous AKA algorithms on the broadcast channel. Additionally, the proposed scheme eliminates the resource consumption of managing complex certificates and addresses the security risks associated with key escrow in the key generation center (KGC). Through security and performance analysis, we demonstrate that our approach can enhance the security of IoT-based healthcare systems while significantly improving communication efficiency. Our proposed scheme provides a promising solution to security issues related to server communication in IoT systems.

The utilization of modular chiplets in interposer-based 2.5-D heterogeneous systems simplifies fabrication and design, however, it also introduces significant noise challenges. This paper presents a collaborative jitter-aware optimization in 2.5-D integrated circuits (ICs), incorporating power supply induced jitter (PSIJ), system impedance, target impedance, and decoupling capacitors, based on the hybrid pre-computation and pre-storage/duplicate removal-non-dominated sorting differential evolution (PCPS/DR-NSDE) algorithm. An automatic channel model algorithm and a uniform decoupling capacitor placement strategy are proposed to improve the design efficiency. Then, the system transfer impedance, simultaneous switch current, sensitivity function, and amplification factor are individually modeled, leading to the assembly and verification of the final PSIJ in the 2.5-D system. A PCPS strategy is proposed to handle high-time-consuming modules in the objective function and a DR operation is added to improve algorithm performance. The proposed PCPS/DR-NSDE is faster than traditional algorithms and has optimal hypervolume and coverage-metric (C-metric) indicators. The procedures for further obtaining desired solutions in the Pareto front are discussed. The impact of practical constraints and target impedance is also analyzed. This work provides a collaborative optimization and analysis of jitter, noise, and impedance in 2.5-D systems.

A large number of multiply-accumulate operations and memory accesses required in deep convolutional neural networks (DCNN) leads to high latency and energy consumption (EC), that hinder their further applications. Dataflow-based acceleration schemes reduce memory accesses by leveraging reusable data in DCNNs. Row Stationary (RS) dataflow is a more advanced dataflow. In the convolutional layer acceleration of RS dataflow, the flexibility of mapping from logical processing element (LPE) sets to physical PE sets is relatively poor. The utilization of processing elements (PEs) is low. In this paper, a novel mapping strategy based on genetic algorithm (GAMS) with the goal of optimizing EC is proposed. GAMS is designed to address the energy inefficiencies faced when mapping RS dataflow. A 3D hybrid optical-electrical Network-on-Chip (3DHOENoC) is proposed to further improve the communication efficiency, energy efficiency and the processing speed of DCNN. Simulation and evaluation results show that GAMS can achieve better mapping flexibility, higher PEs utilization and 15.9% improvement of execution speed on average. In addition, the execution time (ET) performance of processing the DCNN can be further improved by adopting the 3DHOENoC architecture with better communication parallelism.

Aerial computing is gradually playing an essential role in edge and fog computing paradigms by virtue of mobility, availability, scalability, flexibility, and simultaneity, where the Low-altitude Computing (LAC) platform, as the end close to the data sources, is mainly responsible for data collection and storage. However, because of the long physical distance of data transmission and the vulnerability of the transmission link to various attacks, how to efficiently share the stored data while ensuring data privacy is a critical issue for LAC at present. In this paper, we propose a lightweight and secure private data storage and sharing scheme to support range queries over encrypted multi-dimensional data. Specifically, we first propose two data conversion methods for transforming location features and collected log files with multi-dimensional attributes in Unmanned Aerial Vehicles (UAVs). Based on the ideas of asymmetric scalar-product-preserving encryption (ASPE) and inner product comparison (IPC), we design a privacy-preserving storage and sharing technique for the converted data. In addition, to achieve secure and efficient data querying and result verification, we design a secure data index and build a data authentication structure (DAS) with G-tree. Finally, we rigorously analyze the security of our proposed scheme and conduct extensive experiments on a real-world database to prove that our proposed scheme is secure and easy to use in practical application scenarios.

Modern business models improve the efficiency of supply chain management by attaching tags to products. These tagged products typically change owners multiple times during their life cycles. The ownership transfer protocol authorizes the new owner by replacing the old owner’s authentication information stored in the tag with the new owner’s. Until now, a considerable amount of literature has proposed solutions to the problem of RFID ownership transfer. Unfortunately, these existing protocols are either flawed in some security properties especially in protecting new and old owners’ privacy, or are associated with huge computational overheads. In this article, we propose an ultralightweight RFID ownership transfer protocol based on permutation function. The tag and reader only use efficient bit operations, which greatly reduce the computational overhead. An important feature of the proposed protocol is that the new owner can impose calculations on data that has been encrypted by the old owner. The new owner is authorized by the old owner and does not have access to the tag’s key, which protects the old owner’s privacy stored in the tag side. We compare our protocol with existing work, and show the advantages in terms of security, computational overhead, and time cost.

文章提出一种应用于 X 频段和 Ku 频段卫星无线通信的带有滤波效应的新型宽带圆极化天线。该结构包含一个驱动层(同时也是滤波层)以及一个堆叠层(同时也是圆极化层)。带通滤波响应中的两个辐射零点,是衬底集成波导(SIW)腔体支持的开口与嵌入式驱动贴片的综合效果。引入倒角贴片作为堆叠元件,具有同时实现圆极化和拓宽工作带宽的能力。使用多层印制电路板(PCB)工艺制作了一个尺寸为 0.8λ0×0.71λ0×0.16λ0 的紧凑原型进行演示。实验结果与仿真结果吻合良好,测量的 −10-dB 阻抗带宽和 3-dB 轴比带宽分别为10.83% 和 15.54%。此外,还获得了 8.9 dBic 的左旋圆极化峰值增益,大于 7 dBic 的带内平均左旋圆极化增益,以及良好的频率选择性。

In this article, a high-efficient design method of through silicon via (TSV) array for thermal management of 3-dimensional (3-D) integrated system is developed based on the equivalent thermal conductivity (ETC) model and the particle swarm optimization algorithm. Due to the anisotropy of TSV, the ETC model along the vertical direction is established, and the finite element method (FEM) is utilized to validate the accuracy of the ETC model. The relative error of the peak temperature between FEM and ETC model is less than 1.27%, and the average computational time of ETC model is greatly decreased from 261 to 13.3 s. In addition, the TSV array for heat management of 3-D integrated system is investigated and designed by the developed method. According to the designed scheme, the peak and average temperatures of the FEM based on$6\times 6$TSV array are 314.60 and 303.84 K, which will agree with the desired indexes (315 and 310 K). The temperature differences of top and bottom surfaces are all less than 4.3 K, which implies that the thermal distribution of the 3-D integrated system is homogeneous. Therefore, the developed design method can efficiently design TSV array to achieve relatively low and uniform thermal distribution, and it can be applied in the 3-D integrated heat management system.

With the advent of 5G and the Internet of Things, edge computing and cloud computing with their respective strengths are bound as Cloud-Edge-End Orchestrated (CEEO). The CEEO network integrates artificial intelligence and provides innovative technologies for smart grid applications and services. With massive data transmission on the CEEO network, the trustworthiness of the service node exerts an enormous influence on data privacy. To realize securely share data and decrease the local storage, end-user prefer to encrypt data and upload it to the cloud. Meanwhile, the challenge is how to balance efficiency and security perfectly when users need to find relevant documents containing specific keywords from the CEEO network. In this article, we innovatively propose a searchable encryption scheme that supports multi-keyword subset retrieval, named MSIAP. Specifically, we enhance the Apriori, a data mining algorithm, to mine the relevance of files from massive information and build a multi-level index structure. On this basis, we achieve efficient multi-keyword subset retrieval and dynamic update with insignificant information disclosure in the smart grid. Furthermore, our MSIAP strengthens the present data retrieval methods and enormously reduces the time complexity to accommodate the system of distributed smart grid. Finally, we provide the security analysis and performance evaluations by comparing them with existing works.

Biometric applications makes biometric authentication replace the traditional password in many cases. Biometric recognition technology has the advantages of convenience and high stability, facilitating identity recognition. However, the shortcoming of biometric authentication is easy to be stolen and leaked, which raises security concerns. In this paper, we design a lightweight joint biometric authentication scheme (SELBA) based on face and fingerprint. We improve searchable encryption (SE) to protect the privacy security of extracted biometric features in the storage and authentication stage. Because of the problem that biometric features cannot be changed or retrieved once leaked in existing schemes, we propose a cancelable mechanism to reconstruct stolen or damaged biometric templates. Moreover, we make a complete security analysis of the SELBA to meet the confidentiality, renewability, revocability, irreversibility and unlinkability of template in biometric recognition. Meanwhile, we conduct experiments on real data sets to show that SELBA is secure, efficient and easy to use in practical application scenarios.

In spite of achieving promising results in hyperspectral image (HSI) restoration, deep-learning-based methodologies still face the problem of spectral or spatial information loss due to neglecting the inner correlation of HSI. To address this issue, we propose an innovative deep recurrent convolution neural network (DnRCNN) model for HSI destriping. To the best of our knowledge, this is the first study on HSI destriping from the perspective of inner band and interband correlation explorations with the recurrent convolution neural network. In the novel DnRCNN, a selective recurrent memory unit (SRMU) is designed to respectively extract the correlative features involved in spectral and spatial domains. Moreover, an innovative recurrent fusion (RF) strategy incorporated with group concatenation is further proposed to remove strip noise and preserve scene details using the complementary features from SRMU. Experimental results on extensive HSI datasets validated that the proposed method achieves a new state-of-the-art (SOTA) HSI destriping performance.

Enhancing the integration of directional couplers is a crucial challenge in the design of wireless communication circuits and systems. This article proposes a design strategy based on through-silicon via (TSV) insertion and neuro-transfer function (Neuro-TF) modeling to achieve miniaturization of coupled-line couplers. By embedding coupled TSV pairs inside the substrate, a planar coupler can be transformed into a 3-D structure with a smaller footprint. Furthermore, a design flow is proposed to achieve the initial parameters of TSV insertion for various application scenarios. Two insertion schemes with regular and coaxial TSVs are applied to handle different requirements and process constraints. The flow also estimates the number of required TSV pairs based on the area reduction target. To reduce dependence on electromagnetic (EM) simulation and make the strategy more widely applicable to various coupler types, Neuro-TF modeling is utilized for optimization after the initial design. Subsequently, the compatibility between the TSV insertion flow and the Neuro-TF method is analyzed to develop a comprehensive miniaturization strategy. The approach has been validated by two design cases through EM simulations. The results indicate that the proposed strategy enables rapid design and achieves area reduction targets effectively.

As a smart transportation application of the Internet of Things, the Internet of Vehicles (IoV) depresses the chances of traffic accidents, while improving transportation efficiency and user driving experience. However, as the number of vehicles continues to grow, the original ground-based IoV system is difficult to meet the ever-increasing demand. To this end, space–air–ground-integrated network (SAGIN) incorporates satellite systems, aerial network and terrestrial communications. However, because SAGIN integrates multiple network services and communication modes, which makes SAGIN more vulnerable to various types of attacks and security threats. This article first presents the dominating security threats in data storage, transmission and sharing of space–air–ground integrated vehicular network (SAGIVN). Moreover, for guaranteeing the safety and effectiveness of the model, we advance a safe and effective encrypted data retrieval and sharing scheme in SAGIVN (ERDSS) for possible threats, the ERDSS can execute fuzzy retrieval over misspelling keywords and sort results by relevance scores to realize precise retrieval. We perform a comprehensive security discussion and execute experiments based on real-world data sets. The consequences demonstrate that the ERDSS is safe and efficient.

Research on mobile cyber–physical systems (MCPSs) that have the superiorities of cyber–physical systems (CPSs) and expand their application range has become a trend in recent years. The applications of MCPS in fields, such as intelligent transportation systems, smart home appliances, and mobile education, have also become increasingly mature. However, MCPS also has some shortcomings that need to be solved urgently. Sensors carried on mobile devices collect data and upload huge amounts of data to the cloud for statistics and analysis. Mobile devices need to directly interact with the cloud, causing both parties to bear huge computing and communication costs. Since the interaction process includes data sharing and storage, it is particularly important to protect the data itself and the security of sharing. Searchable encryption ensures the safety of communication among the MPEs and the cloud, while protecting the privacy of data on the cloud. However, the existing searchable encryption technology cannot provide efficient and reliable data storage and sharing services for MPEs in MCPS under the premise of ensuring security. In this article, we present a secure and efficient data sharing and privacy protection scheme in MCPS on the basis of the edge computing model (NESPS). Meanwhile, the introduction of edge computing (EC) significantly reduces the communication consumption between the device and the cloud. Furthermore, attribute-based encryption (ABE) enables the NESPS to achieve fine-grained management of device authorities. Moreover, we have carried out security analysis and simulation on the NESPS, the results demonstrate that the NESPS meets the proposed requirements.

Deep convolutional neural networks (DCNNs) have been proposed as enhanced developments of neural networks (NNs) in the field of artificial intelligence (AI) and successfully applied in deep learning (DL) scenarios. With the advancement of technology, the number of network layers has continuously increased, resulting in a huge number of calculations and memory accesses required in the training and inference process of DCNNs and thereby hindering their further deployment and application. Using a specific dataflow formed by reusable DCNN data in the network-on-chip (NoC), reducing the memory access pressure and improving DCNN processing efficiency has become a promising acceleration schemes for the current DCNN. In this paper, a novel convolution layer (CONV) acceleration strategy based on logical PE set segmentation for row stationary (RS) dataflow is proposed to solve the problems of low flexibility and inefficient processing array utilization faced by the conventional folding mapping strategy. The simulation results show that the new mapping strategy based on PE set segmentation can achieve better processing element utilization and CONV acceleration improvement at the expense of little increase in the data movement energy consumption compared with the conventional strategy.

Accurate and fast multiphysics coupling analysis is of great significance to the design of microsystems. In this article, we propose a fast analysis method to improve the computational efficiency of the multiphysics coupling problem in 3-D microsystems. In this method, an iterative dual cell method (IDCM) is developed to deal with electro-thermal coupling problems, and the superposition principle is used to calculate thermal stress. The comparison between the proposed method and the finite element method (FEM) shows that IDCM and superposition principle have high accuracy, and IDCM can reduce the calculation time by up to 30% than FEM. This article studies in detail the changes of temperature and stress with two types of Through silicon via arrays, (TSV) and discusses the influence of the C4-bump layout on the stress distribution. Our work demonstrated that the proposed method can quickly and accurately perform multiphysics coupling analyzes of 3-D microsystems.

Interposer and chiplet-based 2.5-D integrated circuit (IC) designs have become a new trend for block-level heterogeneous integration. In this paper, a new hybrid metaheuristic algorithm named Metropolis-based differential particle swarm optimization (MDP) is designed to jointly optimize the multiconstraints and impedance-based hybrid objective function of chiplet-based 2.5-D IC including interposers, chiplets, through-silicon via (TSV) arrays, bumps, and metal-insulator-metal (MIM) capacitors for simultaneous switch noise (SSN) reduction. Combined with the cascaded PDN assembly method, constraints on routing, delay and proximity distance between the entire system and an impedance-oriented function with multiple critical factors, a hybrid objective function with respect to the 2.5-D PDN is obtained. Integrating the advantages of multiple algorithms, a better hybrid MDP algorithm is designed to optimize the proposed key function. This method adopts the Metropolis rule to avoid the waste of the update mechanism for out-of-boundary particles. The placement, orientation of the chiplets, the on-interposer decoupling capacitor and the constraints of the 2.5-D system are co-optimized to find the optimal solution to eliminate the SSN. The overdesign of the system, different target impedance, different objective-oriented circuit optimization schemes and trade-offs in different constraints are also discussed carefully in this paper for 2.5-D ICs.

This paper introduces deformable convolution in deep learning based joint denoising and demosaicing (JDD), which yields more adaptable representation and larger receptive fields in features extraction for a superior restoration performance. However, the deformable convolution generally leads to considerable computational load and irregular memory access bottleneck, limiting its extensive deployment on edge devices. To address this issue, we develop grouping strategy and assign independent offsets to each kernel group to reduce the computation latency while keeping the accuracy. Motivated by the exploration for aggregate distribution characteristics of deformable offsets, we present the offset sharing methodology to simplify the memory access complexity of deformable convolution. As for hardware acceleration, we specially design a novel deformable matrix multiplication workflow incorporated with a deformable memory mapping unit to boost the computational throughput. The verification experiments on FPGA demonstrate that the proposed deformable convolution based JDD can restore 4K Ultra High Definition (UHD) images at 70FPS and yields significant promotion in visual effect and objective quality assessment.

An original concept of 3-D through-silicon via (TSV)-based Marchand baluns and its design methodology is proposed for on- chip and 3-D integration. By utilizing a 3-D packaging process that includes a TSV and redistribution layer (RDL), a meander coupling path is established and embedded in the vertical direction of the substrate for balun design. The 3-D structure can effectively reduce the on- chip area while maintaining good balance characteristics. Furthermore, the structure can be flexibly integrated with 3-D integrated circuits (3-D ICs) to realize signal conversion between different stacking tiers. An equivalent circuit model based on the TSV-to-TSV coupling channel and coupled transmission line has been established for initial estimation before electromagnetic (EM) optimization. To shorten the design cycle, a specific flow is proposed to meet the structural particularity and different application scenarios. To verify the design method and flow, a design case is analyzed and EM simulated with the antenna feeding network as the target application. The EM simulation results show that the design can work at 43–82 GHz with an amplitude imbalance of less than 0.3 dB and a phase imbalance of less than 1.3°, which meets the requirements of balanced feeding. It only costs a$0.084\,\,\lambda _{\text {g}}\,{\times }\,0.009\,\,\lambda _{\text {g}}$footprint, which is far lower than that of the conventional planar types.

With the rapid growth of data, limited by the storage capacity, more and more IoT applications choose to outsource data to Cloud Service Providers (CSPs). But, in such scenarios, outsourced data in cloud storage can be easily corrupted and difficult to be found in time, which brings about potential security issues. Thus, Provable Data Possession (PDP) protocol has been extensively researched due to its capability of supporting efficient audit for outsourced data in cloud. However, most PDP schemes require the Third-Party Auditor (TPA) to audit data for Data Owners (DOs), which requires the TPA to be trustworthy and fair. To eliminate the TPA, we present a Public Mutual Audit Blockchain (PMAB) for outsourced data in cloud storage. We first propose an audit chain architecture based on Ouroboros and an incentive mechanism based on credit to allow CSPs to audit each other mutually with anticollusion (any CSP is not willing to help other CSPs conceal data problems). Then, we design an audit protocol to achieve public audit efficiently with low cost of audit verification. Rigorous analysis explains the security of PMAB using game theory, and performance analysis shows the efficiency of PMAB using the real-world dataset.

Many artificial intelligence technologies have been applied for extracting useful information from massive industrial big data. However, the privacy issues are usually overlooked in many existing methods. In this article, we propose an anonymous and privacy-preserving federated learning scheme for the mining of industrial big data. We explored the effect of the proportion of shared parameters on the accuracy through experiments, and found that sharing partial parameters can almost achieve the accuracy of sharing all the parameters. On this basis, our proposed federated learning scheme reduces the privacy leakage by sharing fewer parameters between the server and each participant. Specifically, we leverage differential privacy on shared parameters with Gaussian mechanism to provide strict privacy preservation; the effect of different ε and δ on accuracy is tested; and we keep track of δ-when it reaches a certain threshold, training shall be stopped. What's more, we employ a proxy server as the middle layer between the server and all the participants to achieve anonymity of participants; it is worth noting that this can also reduce the communication burden on the federated learning server. Finally, we provide the security analysis and performance evaluations by comparing with other schemes.

Network-on-chip (NoC) has become one of the most common interconnection architectures to integrate multicore system. However, the performance, hardware costs, and power consumption of NoC are sensitive to many parameters, such as topology, the number and depth of virtual channels (VCs), routing algorithms, and flow control mechanisms. In order to find the best NoC solution for different applications, a fast and flexible NoC simulator is necessary. In this article, we present an ultrafast field programmable gate array (FPGA)-based NoC simulator called SRNoC, which is able to be configured via host on PC. In SRNoC, we proposed switch–router architecture and virtual boundary technology. Switch–router is an architecture which makes SRNoC support irregular or custom NoC topology by configuring the NoC topology in hardware. Virtual boundary is a novel configurable virtualization mechanism which can store boundary traffic and use the stored boundary traffic to simulate large NoC in virtualized mode. By employing the switch–router architecture and virtual boundary, SRNoC demonstrates$45\times -3077\times $speed-up against Booksim and maintains the same level of simulation accuracy. This simulator has three main advantages: 1) configured switch–router can support irregular and custom topologies in a nonvirtualized mode; 2) virtual boundary can improve the simulation speed in virtualized mode, effectively; and 3) embedded models can provide evaluation of packet latency, power consumption, and temperature distribution. Due to the switch–router architecture and virtual boundary, SRNoC can simulate a 30-node ($5\times 6$) NoC in nonvirtualized mode and 3072-node ($64\times 48$) in virtualized mode on a Xilinx Virtex-7 FPGA.

In this brief, a fully digitized pulse generator with programmable pulse number and width is used as an ultrawideband (UWB) pulse transmitter with 17.7-dBm output power for see-through-wall (STW) radar. A fully differential operation broadband CMOS power amplifier (PA) with a power-switchable mode and fast settling time for pulse radar is presented, achieving excellent power consumption, considerable output power, and a -3-dB bandwidth from 3.6 to 6.6 GHz. The on-chip transformer is used for impedance transformation in the output and converting signals from the differential to single ended. Fabricated in a 65-nm standard CMOS process, the operating carrier frequency range of the transmitter covers 3.2-5.8 GHz and the pulsewidth can be adjusted from 0.75 to 1.2 ns. The number of pulses varies from 4 to 1. At 10-MHz pulse repetition frequency (PRF), the maximum peak-to-peak voltage of the transmitter output pulse reaches 3.8 V with 50-Ω load, and the corresponding pulsewidth is 0.75 ns. The equivalent power is 17.7-dBm after calibration, and the average power consumption is only 22.5 mW.

As the number of people using social networks increases, users are often assigned to different domains for better digital right management. However, they are no longer satisfied with accessing data just in one domain with the rapid expansion of information, so there is an urgent need for a way to access data among different domains while guarantee their privacy and security. In this paper, we propose a cross-domain access control scheme based on multi-authority attribute-based encryption for big data from social networks. In our construction, we use a hybrid encryption algorithm that based on symmetric encryption and CP-ABE not only to achieve cross-domain access control, but also to improve the efficiency of the system. Besides, the trusted proxy users for proper storage of symmetric keys ensures the security of the system. Meanwhile, the use of an inter-domain trusted proxy user, instead of an intra-domain data owner to formulate an access control policy, reduces the huge computational overhead associated with the ciphertext re-encryption technology and greatly improves the efficiency of the entire system. Finally, security analysis and performance analysis show that the proposed scheme is a secure and trusted networking.

Internet of Things (IoT) has been developed rapidly to make our life easier. In many IoT applications (e.g., smart homes, healthcare, etc.), all the IoT devices should be synchronized in time. However, some malicious nodes located in the IoT network can influence the time synchronization, which may interrupt the IoT system and lead to serious accidents. Therefore, it is critical and challenging to guarantee the accuracy and consistency of time during the time synchronization among all the IoT devices. In this paper, we propose a blockchain-based scheme to assure the security during time synchronization in IoT. Specifically, a publicly verifiable ledger is utilized to record and broadcast time, which can minimize many attacks from external environments. The use of multiple time sources can avoid the vulnerabilities caused by the centralized generation of accurate time. Moreover, the decentralized structure of this scheme has the advantage of adapting the changes of network topology. By employing an improved practical Byzantine fault tolerance consensus mechanism, time synchronization can be implemented efficiently. At last, the analysis results show that our proposed scheme can achieve the desired security with high efficiency.

The high-power dissipation and the low-heat conduction between stacked chips in 3-D structures lead to a high temperature, which has become a critical design constraint for high-performance 3-D integrated circuits (ICs). Naturally, a power distribution network (PDN) in 3-D ICs consists of good heat conductor, but many existing methods do not fully show its thermal removal capability, and the thermal models of a 3-D PDN are imperfect. In this paper, compact and accurate physically based models to compute effective thermal conductivities of a 3-D PDN are proposed. By combining the equivalent thermal conductivity and the proposed complete thermal models for a 3-D PDN, a fast temperature analysis procedure can be expediently applied using the finite volume method. Finite element method (FEM) tools are used to verify the accuracy of the proposed models, and the errors in the temperature responses of our proposed method are within 3% compared with the results from the FEM. In addition, our method also improves the computational efficiency.

Optical networks on chip (ONoC) has been proposed as a promising alternative paradigm for electronic NoC with the benefit of optical signaling communications such as ultrahigh bandwidth, extremely low energy consumption, and negligible transmission latency. To accommodate the layout of tile-based chip multicore processors, a torus-based passive ONoC architecture, TAONoC, is proposed in this paper. Relying on the unique designs of three function modules, TAONoC can still support contention-free communication without the need for arbitration. TAONoC employs comb switches instead of general microring resonators (MRs). TAONoC has a low demand for the number of MRs because of the ultrahigh utilization of resonant wavelengths owned by a single MR. Simulation results show that TAONoC performs well under three different synthetic traffic patterns.

Traditional medical privacy data are at a serious risk of disclosure, and many related cases have occurred over the years. For example, personal medical privacy data can be easily leaked to insurance companies, which not only compromises the privacy of individuals, but also hinders the healthy development of the medical industry. With the continuous improvement of cloud computing and big data technologies, the Internet of Things technology has been rapidly developed. Radio frequency identification (RFID) is one of the core technologies of the Internet of Things. The application of the RFID system to the medical system can effectively solve this problem of medical privacy. RFID tags in the system can collect useful information and conduct data exchange and processing with a back-end server through the reader. The whole process of information interaction is mainly in the form of ciphertext. In the context of the Internet of Things, the paper presents a lightweight RFID medical privacy protection scheme. The scheme ensures security privacy of the collected data via secure authentication. The security analysis and evaluation of the scheme indicate that the protocol can effectively prevent the risk of medical privacy data being easily leaked.

More and more sensitive information of big data are being now stored in the cloud for it offers quality service and convenient management. But the Cloud Server cannot be fully trusted because it may leak information which raises security and privacy concerns of the stored data. Therefore, sensitive data has to be encrypted before shifted to the cloud. It is a great convenience for authorized users to directly retrieve the encrypted data stored in the cloud by keyword search. As we all known, returning all the matching documents is a waste of network bandwidth. Although there are some schemes that support sorting fuzzy multi-keyword search, and they are generally based on edit distance to looking for fuzzy alternatives set, which leads to significantly larger index file size and higher search complexity. Then we rank the search results according to the matching score between keywords and documents. In this paper, we give a novel multi-keyword fuzzy and sortable search scheme. We use n-gram technology to realize fuzzy search and give a novel ranking search scheme based on the fuzzy multi- keyword search. For the matching files may be more than one, there are differences between the original query keywords and the fuzzy keywords. When ranking files, we will not only consider the relevance score between the index keywords and the documents, but also the similarity between the query and keywords. Thus we calculate the comprehensive matching score of the file to the set of query keywords, which is more efficient and accurate.

As one of the most important techniques in IoT, NFC (Near Field Communication) is more interesting than ever. NFC is a short-range, high-frequency communication technology well suited for electronic tickets, micropayment, and access control function, which is widely used in the financial industry, traffic transport, road ban control, and other fields. However, NFC is becoming increasingly popular in the relevant field, but its secure problems, such as man-in-the-middle-attack and brute force attack, have hindered its further development. To address the security problems and specific application scenarios, we propose a NFC mobile electronic ticket secure payment and verification scheme in the paper. The proposed scheme uses a CS E-Ticket and offline session key generation and distribution technology to prevent major attacks and increase the security of NFC. As a result, the proposed scheme can not only be a good alternative to mobile e-ticket system but also be used in many NFC fields. Furthermore, compared with other existing schemes, the proposed scheme provides a higher security.

Researchers and engineers have paid more attention to cloud-based application systems, whose features are virtualization and services provisioning. Fortunately the technology can be just right to healthcare. Meanwhile, security has also become more challenging. Although many cloud-based RFID authentication protocols have been proposed, some of them only improve the function and performance without considering security and privacy, and most of them are heavyweight. It is not appropriate in the field of healthcare, because improving the trustworthiness of anonymous virtual computing services should be the primary consideration. So we propose a lightweight privacy protection authentication scheme which can be applied in the cloud environment, in the scheme, service providers could be anonymous or unknown to the application consumer. Assuming many hospitals build a cloud platform together, the information of patient and his physician will be stored anonymously in the cloud. Patient can go to a doctor in any one hospital with a unique RFID tag. Reader may be fixed or mobile; Readers read tags and upload collected data to the cloud for further processing in real time. Compared with some traditional schemes, our scheme is lightweight, cost-efficient, elastic scalability, real-time, and easy to against synchronization attack.

We present an antenna-in-package system integrated with a meander line antenna based on low temperature co-fired ceramic (LTCC) technology. The proposed system employs a meander line patch antenna, a packaging layer, and a laminated multi-chip module (MCM) for integration of integrated circuit (IC) bare chips. A microstrip feed line is used to reduce the interaction between patch and package. To decrease electromagnetic coupling, a via hole structure is designed and analyzed. The meander line antenna achieved a bandwidth of 220 MHz with the center frequency at 2.4 GHz, a maximum gain of 2.2 dB, and a radiation efficiency about 90% over its operational frequency. The whole system, with a small size of 20.2 mm×6.1 mm×2.6 mm, can be easily realized by a standard LTCC process. This antenna-in-package system integrated with a meander line antenna was fabricated and the experimental results agreed with simulations well.

As one of the core techniques in 5G, the Internet of Things is more interested than ever. Furthermore, radio frequency identification RFID plays a crucial role in Internet of Things development. Although the low-cost RFID system has wide prospect, it has to face with huge challenges because of potential security risks, privacy problems, and efficiency because of its restrictions on processing, storage, and power in RFID tags. One of the possible solutions in secure authentication of the low-cost RFID system is the lightweight RFID authentication protocol. A lightweight RFID mutual authentication protocol with cache in the reader is proposed in this paper, named LRMAPC. The LRMAPC can greatly reduce the computational and transmission cost. Especially, it can reduce computational costs greatly when a large number of tags want to be authenticated. We prove the correctness of LRMAPC using GNY logic. Compared with some existing works, LRMAPC achieves higher efficiency and stronger security. Furthermore, we developed LRMAPC into ULRMAPC, an ultralightweight RFID mutual authentication protocol with cache in the reader. Compared with SASI and Gossamer protocols, ULRMAPC also achieves higher efficiency and stronger security in storage and computation cost. Copyright © 2015 John Wiley & Sons, Ltd.

To increase the performance of chip multiprocessors, optical network-on-chip (ONoC) becomes promising because of its high bandwidth and low energy consumption. In this paper, we propose an architecture called RPNoC (Ring-based Packet-switched NoC), which uses few optical devices. Specifically, Single-waveguide RPNoC employs only one waveguide. Multiwaveguide RPNoC introduces space division multiplexing to make the architecture highly scalable. A novel wavelength assignment method and a deadlock-free deterministic routing algorithm are jointly designed, which make the network diameter quite small. This design also guarantees deadlock freedom, a little resource use, and low complexity at the same time. Evaluation is carried out for the 64-node RPNoC under different synthetic and realistic traffic patterns. The simulation result shows that it yields high throughput and low latency. Comparison with other packet-switched ONoCs shows that RPNoC has the lowest energy consumption.

With the rapid development of the cloud computing, personal health record (PHR) has attracted great attention of many researchers all over the world recently. However, PHR, which is often outsourced to be stored at a third party, has many security and efficiency issues. Therefore, the study of secure and efficient Personal Health Record Scheme to protect users' privacy in PHR files is of great significance. In this paper, we present a secure and efficient Personal Health Record scheme called SE-PHR. In the SE-PHR scheme, we divide the users into personal domain (PSD) and public domain (PUD) logically. In the PSD, the Key-Aggregate Encryption called KAE is exploited. For the users of PUD, we use outsource-able multi-authority attribute-based encryption (MA-ABE) to largely eliminate the overhead for users and support efficient attribute revocation without updating the user's private key. Our scheme also presents a new algorithm which enables dynamic modification of access policies. Function and performance testing results show the security and efficiency of the proposed SE-PHR.

Super-peer p2p networks inherit the advantages of P2P networks, such as pooling together the shared data (documents in our system) across peers, self- organizing, and fault-tolerance. Most of the searches in p2p networks are plaintext-based and do not satisfy the pressing demands for high security in massive information sharing applications. In this paper, a super-peer p2p system is designed that supports ciphertext-based search for relevant documents. In the proposed system, a super-peer which contains all the encrypted index of documents in all the peers subordinate to it is added to the p2p system, and the indexing efficiency is improved by utilizing the indexing structure that binary trees are nested in a B+ tree. Finally, the experimental results demonstrate the proposed scheme can achieve acceptable security and efficiency.

In this brief, a 14-bit 500 MS/s current-steering digital-to-analog converter (DAC) is proposed, which applies the novel grouped random rotation thermometer code (GRTC) and the differential-quad switching (DQS) and has good dynamic performance without calibrations. The GRTC suppresses the harmonics caused by element mismatches, while the DQS reduces the input-code transition-dependent distortion to achieve a high spurious-free dynamic range (SFDR). A unit current cell with an intrinsic precision of 12 bits rather than 14 bits is used to reduce the active area, and the simple diagonal structure with a common-centroid layout is adopted to reduce the gradient error. The measured SFDR of the proposed DAC is more than 80 dBc below 35 MHz and better than 68 dBc over the entire Nyquist bandwidth. The power consumption of the DAC core is only 67.7 mW at 500 MS/s. The proposed DAC has been implemented in the Semiconductor Manufacturing International Corporation (SMIC) 0.18-μm CMOS process and occupies an active area of only 0.55 mm2.

This paper presents a novel multi-output on-chip switched-capacitor (SC) DC-DC converter simultaneously providing two output voltages (2VDD/3 and VDD/3, in addition to the available full VDD) that enables the use of three different power modes for optimizing power/performance trade-offs: super-threshold (full VDD), near-threshold (2VDD/3) and sub-threshold (VDD/3). Unlike previously proposed SC converters, the multiple conversion ratios of the novel converter are achieved without changing the topology of the circuit, thus fewer components being needed to support multiple voltages. Above 84% and 70% efficiencies are obtained in simulation over a range of load currents from 0.4 mA to 5 mA, and from 0.4 mA to 1 mA, with conversion ratios of 2/3 and 1/3, respectively. The paper also presents a novel optimization method to save area by using unequally sized flying capacitors. The target application is for systems with discrete power modes; also for systems that use dithering to emulate a continuous range of voltages such as the Panoptic Dynamic Voltage Scaling (PDVS).

SUMMARY Radio frequency identification (RFID) has been regarded as one of the 10 important technologies in the 21st century. Because of its capability to rapidly and accurately collect and process data in real‐time, RFID has been widely applied in many areas, such as Internet of Things and Smart Grid. However, the existing security threats become more severe toward RFID authentication scheme. The traditional security mechanisms cannot be used in RFID directly because of the limitations of processing capability, storage space, and power supply of RFID tags. In this paper, we propose a revocable secure efficient lightweight RFID authentication scheme (RSEL). To achieve authentication efficiency, the key of the tag is chosen to reduce the number of hash computing in the database. Furthermore, the key is stored in the database and updated constantly with the tag to prevent the tracking and synchronization attacks. The valid period of each tag is stored in the database so that RSEL can revoke the expired tag. The correctness of RSEL has been proved using GNY logic. The performance of RSEL in terms of security and efficiency is evaluated. Compared with other existing approaches, RSEL achieves stronger security and higher efficiency. Copyright © 2013 John Wiley & Sons, Ltd.

A 19 mW highly integrated GPS receiver with a ΣΔfractional-N synthesizer is presented in this paper. Fractional-N frequency synthesizer architecture was adopted in this work, to provide more degrees of freedom in the synthesizer design. A high linearity low noise amplifier (LNA) is integrated into the chip. The radio receiver chip was fabricated in a 0.18 μm complementary metal oxide semiconductor (CMOS) process and packaged in a 48-pin 2 mm×2 mm land grid array chip scale package. The chip consumes 19 mW (LNA1 excluded) and the LNA1 6.3 mW. Measured performances are: noise figure<2 dB, channel gain=108 dB (LNA1 included), image rejection>36 dB, and −108 dBc/Hz @ 1 MHz phase noise offset from the carrier. The carrier noise ratio (C/N) can reach 41 dB at an input power of −130 dBm. The chip operates over a temperature range of [−40, 120] °C and ±5% tolerance over the CMOS technology process.