During the past decade, the Internet of Things (IoT) has paved the way for the ongoing digitization of society in unique ways. Its penetration into enterprise and day-to-day lives improved the supply chain in numerous ways. Unfortunately, the profuse diversity of IoT devices has become an attractive target for malware authors who take advantage of its vulnerabilities. Accordingly, enhancing the security of IoT devices has become the primary objective of industrialists and researchers. However, most present studies lack a deep understanding of IoT malware and its various aspects. As understanding IoT malware is the preliminary base of research, in this work, we present an IoT malware taxonomy with 100 attributes based on the IoT malware categories, attack types, attack surfaces, malware distribution architecture, victim devices, victim device architecture, IoT malware characteristics, access mechanisms, programming languages, and protocols. In addition, we have mapped these categories into 77 IoT Malwares identified between 2008 and 2022. Furthermore, To provide insight into the challenges in IoT malware research for future researchers, our study also reviews the existing IoT malware detection works.

The detection of anomaly traffic in internet of things (IoT) is mainly based on the original binary data at the traffic packet level and the structured data at the session flow level. This kind of dataset has a single feature extraction method and relies on prior manual knowledge. It is easy to lose critical information during data processing, which reduces the validity and robustness of the dataset. In this paper, we first construct a new anomaly traffic dataset based on the traffic packet and session flow data in the Iot-23 dataset. Second, we propose a feature extraction method based on feature fluctuation. Our proposed method can effectively solve the disadvantage that the data collected in different scenarios have different characteristics, which leads to the feature containing less information. Compared with the traditional anomaly traffic detection model, experiments show that our proposed method based on feature fluctuation has stronger robustness, can improve the accuracy of anomaly traffic detection and the generalization ability of the traditional model, and is more conducive to the detection of anomalous traffic in IoT.

Voice assistant system (VAS) is a popular technology for users to interact with the Internet and the Internet-of-Things devices. In the VAS, voice queries are linked to users' accounts, resulting in long-term and continuous profiling at the service provider. In this paper, we propose a VAS anonymizer aiming to mix the queries of the VAS users to increase the source anonymity. The VAS anonymizer is equipped with a pattern-matching scheme, which allows VAS devices to find effective peer relays without disclosing their query patterns. Furthermore, the VAS anonymizer is equipped with anonymity evaluation modules for evaluating real-time single query, thus reducing the risk of pattern violation at the relays. Both the requester and the relay will evaluate the real-time query based on the resulting anonymity. Only if the anonymity evaluations at both requester and relay are positive, the query will be sent to the service provider via the relay. The VAS anonymizers at VAS devices coordinate the query uploading such that the sources of the queries are anonymized, and the service provider is unable to link the voice queries to individual users. In the experiments using our customized VAS devices and the Amazon Cloud servers, the computation and communication overhead of the matching scheme is shown to be efficient, and the anonymity evaluation modules are shown to be effective in protecting the privacy of the requesters and the relays.

Healthcare innovations are increasingly becoming reliant on high variety and standards-compliant (e.g., HIPAA, common data model) distributed data sets that enable predictive analytics. Consequently, health information systems need to be developed using cooperation and distributed trust principles to allow protected data sharing between multiple domains or entities (e.g., health data service providers, hospitals and research labs). In this paper, we present a novel health information sharing system viz., HonestChain that uses Blockchain technology to allow organizations to have incentive-based and trustworthy cooperation to either access or provide protected healthcare records. More specifically, we use a consortium Blockchain approach coupled with chatbot guided interfaces that allow data requesters to: (a) comply with data access standards, and (b) allow them to gain reputation in a consortium. We also propose a reputation scheme for creation and sustenance of the consortium with peers using Requester Reputation and Provider Reputation metrics. We evaluate HonestChain using Hyperledger Composer in a realistic simulation testbed on a public cloud infrastructure. Our results show that our HonestChain performs better than the state-of-the-art requester reputation schemes for data request handling, while choosing the most appropriate provider peers. We particularly show that HonestChain achieves a better tradeoff in metrics such as service time and request resubmission rate. Additionally, we also demonstrate the scalability of our consortium platform in terms of the Blockchain transaction times.

In Blockchain networks involving multiple applications, the quality of service of an application is affected by the transaction ordering. For instance, upon issuing payment transactions, users of an application would like to be notified quickly on the transactions approval. The application can be a financial institution (such as a bank), sharing the blockchain with other such applications and is represented by a node. A node might attempt to prioritize its own transactions by including them early in blocks added to the blockchain. A fair block proposal of a node follows a random selection of the transactions among the set of pending transactions the node is aware of. On the contrary, a dishonest node includes more of its transactions at the expense of transactions of other applications. In this work, we propose a toolbox of techniques to enforce such a fair block selection. First, we design an accurate statistical test for the honesty of a proposal and explain it. We conduct experiments demonstrating the accuracy of the new validation scheme. We also describe a technique that enforces fair block selection through concise commitments on the set of pending transactions known to a node. We clarify the advantages of the new mechanisms over state-of-the-art methods.

In industrial production, personal protective equipment (PPE) protects workers from accidental injuries. However, wearing PPE is not strictly enforced among workers due to all kinds of reasons. To enhance the monitoring of workers and thus avoid safety accidents, it is essential to design an automatic detection method for PPE. In this paper, we constructed a dataset called FZU-PPE for our study, which contains four types of PPE (helmet, safety vest, mask, and gloves). To reduce the model size and resource consumption, we propose a lightweight object detection method based on deep learning for superfast detection of whether workers are wearing PPE or not. We use two lightweight methods to optimize the network structure of the object detection algorithm to reduce the computational effort and parameters of the detection model by 32% and 25%, respectively, with minimal accuracy loss. We propose a channel pruning algorithm based on the BN layer scaling factor to further reduce the size of the detection model. Experiments show that the automatic detection of PPE using our lightweight object detection method takes only 9.5 ms to detect a single video frame and achieves a detection speed of 105 FPS. Our detection model has a minimum size of 1.82 MB and a model size compression rate of 86.7%, which can meet the strict requirements of memory occupation and computational resources for embedded and mobile devices. Our approach is a superfast detection method for green edge computing.

The -nearest neighbor ( -NN) query is an important query in location-based service (LBS), which can query the nearest points to a given point, and provide some convenient services such as interest recommendations. Hence the privacy protection issue of -NN query has been a popular research area, protecting the information of queries and the queried results, especially in the information era. However, most of existing schemes fail to consider the privacy protection of location points already stored on servers. Or some schemes support no update of location points. In this paper, we present an updatable and privacy-preserving -NN query scheme to address the above two issues. Concretely, our scheme utilizes the D-tree ( -Dimensional tree) to store the location points of data owners in location service provider and encrypts the points with a distributed double-trapdoor public-key cryptosystem. Then, based on the Ciphertext Comparison Protocol and Ciphertext Euclidean Distance Calculation Protocol, our scheme can protect the privacy of location and query contents. Experimental analyses show our proposal supports some new location points for a fixed location service provider. Moreover, the queried results show a high accuracy of more than 95%.

As blockchain technology booms, modern electronic voting system leverages blockchain as underlying storage model to make the voting process more transparent, and guarantee immutability of data. However, the transparent characteristic may disclose sensitive information of candidate for all system users have the same right to their information. Besides that, the pseudo-anonymity of blockchain will lead to the disclosure of voters' privacy and the third-parties such as registration institutions involved in voting process also have possibility of tampering data. To overcome these difficulties, we apply authority management mechanism into blockchain-based voting systems. In this paper, we put forward AMVchain, a fully decentralized and efficient blockchain-based voting system. AMVchain has a three-layer access control architecture, and on each layer, smart contracts are responsible for validation and granting permissions. Linkable ring signature is adopted in the process of voting to protect ballot-privacy. AMVchain also makes a tradeoff between efficiency and concurrency by introducing proxy nodes. The experiments results show that our system meets the basic requirements under the high concurrent users circumstance.

One of the major concerns in any emergency relief operation is appropriate allocation of scarce emergency relief materials to the affected community. Due to several reasons ranging from lack of mechanism to accurately assess demand and utility of relief materials to malicious participation of some of the stakeholders, such allocation may become ad-hoc. Thus, it becomes imperative to have an unchallengeable and globally accessible record of relief requirement vis-à-vis allocation for efficient relief management. Emergency response organizations (e.g. UNICEF) have recommended the adoption of blockchain technology to create such immutable records. However, the usage of blockchain is restricted by the availability of end-to-end internet connection which may not be available in a post-disaster scenario. This paper proposes ReliefChain, a blockchain leveraged post disaster relief allocation system over delay tolerant network that works in such environments. We validate relief requirements to mitigate resource diversion, forecasting the exact demand and enumerating precise utilities of relief items. We design smart contracts for creating new transactions to upload relief requirements and allocations in the blockchain network. The proposed system executes these smart contracts to create an immutable and globally accessible record of relief requirement and allocation. Effectiveness of the proposed system is evaluated through extensive simulation in Ethereum platform. Results substantiate the efficiency of the system over a system using baseline methodologies, in terms of design parameters like shelter specific deficit and average resource deficit while not compromising the blockchain performance in terms of processing time and gas consumption even in presence of malicious forwarders.

With the development of digital healthcare, sharing electronic medical record data has become an indispensable part of improving medical conditions. Aiming at the centralized power caused by the single attribute authority in current CP-ABE schemes and the problem that cloud servers are curious and even malicious, we design a revocable CP-ABE EHR sharing scheme with multiple authorities (MA-RABE) in blockchain. In this solution, a group of authorities complete user attribute distribution, key generation and user management through secret sharing and transactions. Besides, we innovatively implemented a distributed one-way anonymous key agreement so that other participants cannot obtain useful information from the fully hidden policy embedded in the ciphertext. Taking into account the computational overhead of a large number of bilinear operations in the decryption process, the solution also supports the cloud server to pre-decrypt the ciphertext, and the data user only needs to perform exponentiation operation once to obtain the plaintext from the pre-decryption result. Theoretical analysis and performance evaluation show that the scheme has reliable security and lower user revocation and ciphertext update overhead.

Technologies like Internet of Things (IoT), cloud, artificial intelligence, blockchain etc. have become a perceptible part of our lives resulting in the generation of enormous amounts of data. Consequently, the systems used for storage and processing of this data are required to be scalable for handling the huge volumes of data. A shared, multitenant system such as a cloud-based storage-as-a-service provides scalability of storage as well as economics of sharing. However, there is a risk of data tampering when multiple tenants work in a shared environment. The benefits of a multitenant solution can be leveraged only if tenants' data is isolated from each other. Further, prevention of data tampering from malicious tenant nodes is also required. Therefore, the paper proposes the use of a private blockchain for an implementation of a multi-tenant-based storage system. The objective is to develop a scalable system where tenants' data is not at a risk of tampering. The efficacy of the proposed system has been demonstrated with synthetic data of multiple tenants using a Software as a Service (SaaS) healthcare application.

Nowadays, securely sharing medical data is one of the significant concerns in blockchain technology. The existing blockchain approaches have faced high time consumption, low confidentiality, and high memory usage for transferring the file in a secure way because of attack harmfulness and large unstructured records. It has ended in security threat, so the integrity of the user data has been lost. Hence, a novel hybrid Deep Belief-based Diffie Hellman (DBDH) security framework was presented to protect medical data from malicious events. Incorporating a deep belief neural system continuously monitors the system and identifies the attacks. Initially, the IoMT dataset was collected from the standard site and imported into the system. Moreover, hash 1 was calculated for the original data and stored in the cloud server for verification. Then, the original data was encrypted with a private key for data hiding. The incorporation of homomorphic property helps to calculate hash 2 for encrypted data. Finally, in the verification module, both hash values are verified. In addition, cryptanalysis was performed by launching an attack to validate the performance of the designed model. Moreover, the estimated outcomes of the presented model were compared with existing approaches to determine the improvement score.

Graph Neural Network (GNN) architecture is a state-of-the-art model, which can obtain complete node embedding features and rich data information by aggregating the information of nodes and neighbors. Therefore, GNNs are widely used in electronic shopping, drug discovery (especially for the treatment of COVID-19) and other fields, promoting the explosive development of machine learning. However, user interaction, data sharing and circulation are highly sensitive to privacy, and centralized storage can lead to data isolation. Therefore, Federated Learning with high efficiency and strong security and privacy enhancement technology based on secure aggregation can improve the security dilemma faced by GNN. In this paper, we propose an Efficient Secure Aggregation for Federated Graph Neural Network(ESA-FedGNN), which can efficiently reduce the cost of communication and avoid computational redundancy while ensuring data privacy. Firstly, a novel secret sharing scheme based on numerical analysis is proposed, which employs Fast Fourier Transform to improve the computational power of the neural network in sharing phase, and leverages Newton Interpolation method to deal with the disconnection and loss of the client in reconstruction phase. Secondly, a regular graph embedding based on geometric distribution is proposed, which optimizes the aggregation speed by using data parallelism. Finally, a double mask is adopted to ensure privacy and prevent malicious adversaries from stealing model parameters. We achieve improvements compared to in state-of-the-art works. This research helps to provide security solutions related to the practical development and application of privacy-preserving graph neural network technology.

Traditional healthcare services have transitioned into modern healthcare services where doctors remotely diagnose the patients. Cloud computing plays a significant role in this change by providing easy access to patients' medical records to all stakeholders, such as doctors, nurses, patients, life insurance agents, etc. Cloud services are scalable, cost-effective, and offer a broad range of mobile access to patients' electronic health record (EHR). Despite the cloud's enormous benefits like real-time data access, patients' EHR security and privacy are major concerns. Since the information about patients' health is highly sensitive and crucial, sharing it over the unsecured wireless medium brings many security challenges such as eavesdropping, modifications, etc. Considering the security needs of remote healthcare, this paper proposes a robust and lightweight, secure access scheme for cloud-based E-healthcare services. The proposed scheme addresses the potential threats to E-healthcare by providing a secure interface to stakeholders and prohibiting unauthorized users from accessing information stored in the cloud. The scheme makes use of multiple keys formed through the key derivation function (KDF) to ensure end-to-end ciphering of information for preventing misuse. The rights to access the cloud services are provided based on the identity and the association between stakeholders, thus ensuring privacy. Due to its simplicity and robustness, the proposed scheme is the best fit for protecting data security and privacy in cloud-based E-healthcare services.

With the rapid development of wireless communication and edge computing, UAV-assisted networking technology has great significance in many application scenarios such as traffic forecasting, emergency rescue, military reconnaissance. However, due to dynamic topology changes of Flying Ad-hoc Networks (FANET), frequent identity authentication is easy to cause the instability of communications between UAV nodes, which makes FANET face serious identity security threats. Therefore, it is an inevitable trend to build a secure and reliable FANET. In this paper, we propose a lightweight mutual identity authentication scheme based on adaptive trust strategy for Flying Ad-hoc Networks (ATS-LIA), which selects the UAV with the highest trust value from the UAV swarm to authenticate with the ground control station (GCS). While ensuring the communication security, we reduce the energy consumption of UAV to the greatest extent, and reduce the frequent identity authentication between UAV and GCS. Through the security game verification under the random oracle model, it is proved that the proposed method can effectively resist some attacks, effectively reduce the computational overhead, and ensure the communication security of FANET. The results show that compared with the existing schemes, the proposed ATS-LIA scheme has lower computational overhead.

In recent years, smartphone users are interested in large volumes to view live videos and sharing video resources over social media (e.g., Youtube, Netflix). The continuous streaming of video in mobile devices faces many challenges in network parameters namely bandwidth estimation, congestion window, throughput, delay, and transcoding is a challenging and time-consuming task. To perform these resource-intensive tasks via mobile is complicated, and hence, the cloud is integrated with smartphones to provide Mobile Cloud Computing (MCC). To resolve the issue, we propose a novel framework called delay aware bandwidth estimation and intelligent video transcoder in mobile cloud. In this paper, we introduced four techniques, namely, Markov Mobile Bandwidth Cloud Estimation (MMBCE), Cloud Dynamic Congestion Window (CDCW), Queue-based Video Processing for Cloud Server (QVPS), and Intelligent Video Transcoding for selecting Server (IVTS). To evaluate the performance of the proposed algorithm, we implemented a testbed using the two mobile configurations and the public cloud server Amazon Web Server (AWS). The study and results in a real environment demonstrate that our proposed framework can improve the QoS requirements and outperforms the existing algorithms. Firstly, MMBCE utilizes the well-known Markov Decision Process (MDP) model to estimate the best bandwidth of mobile using reward function. MMBCE improves the performance of 50% PDR compared with other algorithms. CDCW fits the congestion window and reduces packet loss dynamically. CDCW produces 40% more goodput with minimal PLR. Next, in QVPS, the M/M/S queueing model is processed to reduce the video processing delay and calculates the total service time. Finally, IVTS applies the M/G/N model and reduces 6% utilization of transcoding workload, by intelligently selecting the minimum workload of the transcoding server. The IVTS takes less time in slow and fast mode. The performance analysis and experimental evaluation show that the queueing model reduces the delay by 0.2 ms and the server's utilization by 20%. Hence, in this work, the cloud minimizes delay effectively to deliver a good quality of video streaming on mobile.

With the increasingly complex social situation, the problems of traditional online public opinion governance are increasingly serious. Especially the problem of transmission efficiency, public opinion data management and user information security of Internet users is urgently needed. Here, we design a functional infrastructure framework of the network public opinion collaborative governance model based on the blockchain with strong practicality and comprehensiveness. In order to reach the consensus mechanism requirements under the framework, the algorithm is improved on the basis of the defects of the traditional DPoS consensus algorithm. Considering time dynamic factors in the process of reaching consensus, the paper proposes a reputation-based voting model. Furthermore, the paper purposes a rewards and punishments incentive mechanism, and also designs a new method of counting votes. From the simulation results, it was found that after the improvement of the algorithm, the enthusiasm of node participation was significantly increased, the proportion of error nodes was significantly reduced, and the operating efficiency was significantly improved. It shows that the improved consensus algorithm we propose applies to public opinion governance can not only improve the security of the system with the reduce of false public opinion spreading, but also improve the efficiency of information processing, so it can be well applied to information sharing and public opinion governance scenarios.

In recent years, the rapid development of blockchain technology and cryptocurrencies has influenced the financial industry by creating a new crypto-economy. Then, next-generation decentralized applications without involving a trusted third-party have emerged thanks to the appearance of smart contracts, which are computer protocols designed to facilitate, verify, and enforce automatically the negotiation and agreement among multiple untrustworthy parties. Despite the bright side of smart contracts, several concerns continue to undermine their adoption, such as security threats, vulnerabilities, and legal issues. In this paper, we present a comprehensive survey of blockchain-enabled smart contracts from both technical and usage points of view. To do so, we present a taxonomy of existing blockchain-enabled smart contract solutions, categorize the included research papers, and discuss the existing smart contract-based studies. Based on the findings from the survey, we identify a set of challenges and open issues that need to be addressed in future studies. Finally, we identify future trends.

IoT services are the basic building blocks of smart cities, and some of such crucial services are provided by smart buildings. Most of the services like smart meters, indoor navigation, lighting control, etc., which contribute to smart buildings, need the locations of people or objects within the building. This gave rise to Indoor Localization, where only the infrastructure of the building has to be used for localization as accessing the Global Positioning System is difficult in indoor environments. Many approaches have been proposed to predict locations based on the infrastructure available indoors, and some of such techniques use Wi-Fi access points. Still, unfortunately, very few studies have concentrated on tolerating faults while being cost-effective. This work discusses hardware implementation of indoor localization. It then proposes a learning algorithm SRNN (Speed Conscious Recurrent Neural Network) that uses the RSSI (Received Signal Strength Indicator) values of available Wi-Fi access points in the building and predicts the location. Also, fault-tolerant approaches termed and the using Kullback-Leibler Divergence have been proposed to improve the location accuracy when access points go down and are prone to faults. Both the proposed approaches and along with SRNN improve the location accuracy by 4% and 2.1%, respectively, over existing techniques and contribute to optimizing predicted location's accuracy in Indoor Localization an IoT service for smart buildings.

The focus of this paper is to design and develop a Peer-to-Peer Presentation System (P2P-PS) that supports E-learning through live media streaming coupled with a P2P shared whiteboard. The participants use the "ask doubt" feature to raise and resolve doubts during a session of ongoing presentation. The proposed P2P-PS system preserves causality between ask doubt and its resolution while disseminating them to all the participants. A buffered approach is employed to enhance the performance of P2P shared whiteboard, which may be used either in tandem with live media streaming or in standalone mode. The proposed system further extends P2P interactions on stored contents (files) built on top of a P2P file sharing and searching module with additional features. The added features allow the creation of mash-up presentations with annotations, posts, comments on audio, video, and PDF files as well as a discussion forum. We have implemented the P2P file sharing and searching system on the de Bruijn graph-based overlay for low latency. Extensive experiments were carried out on Emulab to validate the P2P-PS system using 200 physical nodes.

Blockchain technology promises enhanced data ownership, control, and interoperability in healthcare, yet security and privacy concerns continue to hinder its adoption. Existing surveys examine blockchain-based privacy challenges, but they lack a systematic analysis and maturity evaluation of privacy-preserving techniques tailored to healthcare data sharing. This paper presents a systematic review of blockchain-based privacy-preserving solutions, analyzing blockchain details, applied privacy methods, regulatory compliance, and maturity levels using Technology Readiness Levels (TRLs). Our findings reveal that authentication and authorization is the most explored stage, dominated by smart contracts and ciphertext-policy attribute-based encryption. Proxy re-encryption is frequently used for data transfer, while privacy-preserving search and verification remain underexplored. On/off-chain mechanisms are commonly applied to balance privacy and storage efficiency. TRL assessment shows that most solutions remain at the proof-of-concept stage (TRL3), with only limited progress to prototype validation (TRL4-TRL5), highlighting the gap between experimental designs and real-world deployment. To guide developers and researchers, we identify two primary patterns of blockchain integration and propose a framework for system design. We also compare methods across data-sharing stages, outlining their strengths and limitations to support informed selection. In conclusion, while research interest is growing, the field remains at an early stage of maturity. Addressing this gap requires stronger implementation capacity, access to clinical data, and robust regulatory alignment. We emphasize the importance of clinical validation and real-world testing to advance privacy-preserving blockchain solutions toward practical adoption in healthcare.