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The past decades have witnessed a wide application of federated learning in crowd sensing, to handle the numerous data collected by the sensors and provide the users with precise and customized services. Meanwhile, how to protect the private information of users in federated learning has become an important research topic. Compared with the differential privacy (DP) technique and secure multiparty computation (SMC) strategy, the covert communication mechanism in federated learning is more efficient and energy-saving in training the machine learning models. In this paper, we study the covert communication problem for federated learning in crowd sensing Internet-of-Things networks. Different from the previous works about covert communication in federated learning, most of which are considered in a centralized framework and experimental-based, we firstly proposes a centralized covert communication mechanism for federated learning among n learning agents, the time complexity of which is O(log n), approximating to the optimal solution. Secondly, for the federated learning without parameter server, which is a harder case, we show that solving such a problem is NP-hard and prove the existence of a distributed covert communication mechanism with O(log log Δ log n) times, approximating to the optimal solution. Δ is the maximum distance between any pair of learning agents. Theoretical analysis and numerical simulations are presented to show the performance of our covert communication mechanisms. We hope that our covert communication work can shed some light on how to protect the privacy of federated learning in crowd sensing from the view of communications.

Decentralized Storage Networks (DSNs) represent a paradigm shift in data storage methodology, distributing and housing data across multiple network nodes rather than relying on a centralized server or data center architecture. The fundamental objective of DSNs is to enhance security, reinforce reliability, and mitigate censorship risks by eliminating a single point of failure. Leveraging blockchain technology for functions such as access control, ownership validation, and transaction facilitation, DSN initiatives aim to provide users with a robust and secure alternative to traditional centralized storage solutions. This paper conducts a comprehensive analysis of the developmental trajectory of DSNs, focusing on key components such as Proof of Storage protocols, consensus algorithms, and incentive mechanisms. Additionally, the study explores recent optimization tactics, encountered challenges, and potential avenues for future research, thereby offering insights into the ongoing evolution and advancement within the DSN domain.

The privacy concern in smart contract applications continues to grow, leading to the proposal of various schemes aimed at developing comprehensive and universally applicable privacy-preserving smart contract (PPSC) schemes. However, the existing research in this area is fragmented and lacks a comprehensive system overview. This paper aims to bridge the existing research gap on PPSC schemes by systematizing previous studies in this field. The primary focus is on two categories: PPSC schemes based on cryptographic tools like zero-knowledge proofs, as well as schemes based on trusted execution environments. In doing so, we aim to provide a condensed summary of the different approaches taken in constructing PPSC schemes. Additionally, we also offer a comparative analysis of these approaches, highlighting the similarities and differences between them. Furthermore, we shed light on the challenges that developers face when designing and implementing PPSC schemes. Finally, we delve into potential future directions for improving and advancing these schemes, discussing possible avenues for further research and development.

Metaverse has rekindled human beings’ desire to further break space-time barriers by fusing the virtual and real worlds. However, security and privacy threats hinder us from building a utopia. A metaverse embraces various techniques, while at the same time inheriting their pitfalls and thus exposing large attack surfaces. Blockchain, proposed in 2008, was regarded as a key building block of metaverses. it enables transparent and trusted computing environments using tamper-resistant decentralized ledgers. Currently, blockchain supports Decentralized Finance (DeFi) and Non-fungible Tokens (NFT) for metaverses. However, the power of a blockchain has not been sufficiently exploited. In this article, we propose a novel trustless architecture of blockchain-enabled metaverse, aiming to provide efficient resource integration and allocation by consolidating hardware and software components. To realize our design objectives, we provide an On-Demand Trusted Computing Environment (OTCE) technique based on local trust evaluation. Specifically, the architecture adopts a hypergraph to represent a metaverse, in which each hyperedge links a group of users with certain relationship. Then the trust level of each user group can be evaluated based on graph analytics techniques. Based on the trust value, each group can determine its security plan on demand, free from interference by irrelevant nodes. Besides, OTCEs enable large-scale and flexible application environments (sandboxes) while preserving a strong security guarantee.

Recent years have witnessed an increasing interest in the blockchain technology, and many blockchain-based applications have been developed to take advantage of its decentralization, transparency, fault tolerance, and strong security. In the field of smart grids, a plethora of proposals have emerged to utilize blockchain for augmenting intelligent energy management, energy trading, security and privacy protection, microgrid management, and energy vehicles. Compared with traditional centralized approaches, blockchain-based solutions are able to exploit the advantages of blockchain to realize better functionality in smart grids. However, the blockchain technology itself has its disadvantages in low processing throughput and weak privacy protection. Therefore, it is of paramount importance to study how to integrate blockchain with smart grids in a more effective way so that the advantages of blockchain can be maximized and its disadvantages can be avoided.This article surveys the state-of-the-art solutions aiming to integrate the emergent blockchain technology with smart grids. The goal of this survey is to discuss the necessity of applying blockchain in different components of smart grids, identify the challenges encountered by current solutions, and highlight the frameworks and techniques used to integrate blockchain with smart grids. We also present thorough comparison studies among blockchain-based solutions for smart grids from different perspectives, with the aim to provide insights on integrating blockchain with smart grids for different smart grid management tasks. Finally, we list the current projects and initiatives demonstrating the current effort from the practice side. Additionally, we draw attention to open problems that have not yet been tackled by existing solutions, and point out possible future research directions.

Spectrum auction is one of the most effective ways to achieve dynamic spectrum allocation in cognitive radio networks, and it provides one effective way to manage the spectrum demands of IoT devices with limited resources. Most spectrum auctions focus on protecting bidder privacy and achieving excellent social efficiency, but few tackles the verification of auction results that are controlled by the auctioneer. In this paper, we propose a cloud-based framework for verifiable privacy-preserving spectrum auctions. Our framework adopts a modified AFGH re-encryption algorithm that achieves both bid privacy protection and auction results verification at the same time. The cloud server helps to compute auction results based on homomorphic encryption, and an auctioneer decrypts the encrypted data from the server to obtain auction results. Meanwhile, the property of re-encryption makes it possible for any bidder to verify the auction results without compromising other bidders’ privacy.

Community search has been extensively studied in large networks, such as Protein-Protein Interaction (PPI) networks, citation graphs, and collaboration networks. However, in terms of widely existing multi-valued networks, where each node has d (d⩾1) numerical attributes, almost all existing algorithms either completely ignore the attributes of node at all or only consider one attribute. To solve this problem, the concept of skyline community was presented, based on the concepts of k-core and skyline recently. The skyline community is defined as a maximal k-core that satisfies some influence constraints, which is very useful in depicting the communities that are not dominated by other communities in multi-valued networks. However, the algorithms proposed on skyline community search can only work in the special case that the nodes have different values on each attribute, and the computation complexity degrades exponentially as the number of attributes increases. In this work, we turn our attention to the general scenario where multiple nodes may have the same attribute value. Specifically, we first present an algorithm, called MICS, which can find all skyline communities in a multi-valued network. To improve computation efficiency, we then propose a dimension reduction based algorithm, called P-MICS, using the maximum entropy method. Our algorithm can significantly reduce the skyline community searching time, while is still able to find almost all cohesive skyline communities. Extensive experiments on real-world datasets demonstrate the efficiency and effectiveness of our algorithms.

With JavaScript being the most popular programming language on the web, several new JavaScript frameworks are released every year. A well designed framework may help developers create secure applications. The goal of our study is to understand how framework developers can best protect applications developed using their framework. In this work we studied how cross-site request forgery vulnerability is mitigated in several server-side JavaScript frameworks: Express.js, Koa.js, Hapi.js, Sails.js, and Meteor.js. We then analyzed open source applications developed with these frameworks using open source and custom written tools for automated static analysis and identified the percentage of protected applications for each framework. We correlated our analysis results to the implementation levels of mitigating controls in each framework and performed statistical analysis of our results to ensure no other confounding factors were involved. Based on the received outcomes we provide recommendations for framework developers on how to create frameworks that produce secure applications.

JavaScript applications today are not limited to just client-side web applications and server-side code powered by Node.js. They became the standard for desktop application development with the emergence and popularity of the Electron framework. Combining the features of client-side and server-side applications, the Electron applications possess a completely different security posture. The attacks typical for front-end applications can now be escalated to the back-end attacks, for example, making a cross-site scripting result in a remote code execution on the user’s machine. The goal of our study is to analyze the typical security vulnerabilities of an Electron application, study common mitigation controls, and propose new remediation solutions that are easy to implement for developers. In this study we analyze security vulnerabilities in over a hundred open source Electron applications using automated and manual static analysis. We explore the mitigation controls existing in the Electron framework, and propose changes to the framework that will prevent many of the common vulnerabilities. Based on these results, we develop an IDE plugin for Electron applications that automatically suggests remediations to common security defects within a developer’s work environment, thus shifting the fixing of a vulnerability to earlier in the software development life cycle. We show the effectiveness of the IDE plugin by applying the plugin’s suggestions to the analyzed open source applications and demonstrating that they stop being exploitable after the applied fix.

In distributed peer-to-peer (P2P) applications, peers self-organize and cooperate to effectively complete certain tasks such as forwarding files, delivering messages, or uploading data. Nevertheless, users are selfish in nature and they may refuse to cooperate due to their concerns on energy and bandwidth consumption. Thus each user should receive a satisfying reward to compensate its resource consumption for cooperation. However, suitable incentive mechanisms that can meet the diverse requirements of users in dynamic and distributed P2P environments are still missing. On the other hand, we observe that Blockchain is a decentralized secure digital ledger of economic transactions that can be programmed to record not just financial transactions and Blockchain-based cryptocurrencies get more and more market capitalization. Therefore in this paper, we propose a Blockchain based truthful incentive mechanism for distributed P2P applications that applies a cryptocurrency such as Bitcoin to incentivize users for cooperation. In this mechanism, users who help with a successful delivery get rewarded. As users and miners in the Blockchain P2P system may exhibit selfish actions or collude with each other, we propose a secure validation method and a pricing strategy, and integrate them into our incentive mechanism. Through a game theoretical analysis and evaluation study, we demonstrate the effectiveness and security strength of our proposed incentive mechanism.

Fog computing is deemed as a highly virtualized paradigm that can enable computing at the Internet of Things devices, residing in the edge of the network, for the purpose of delivering services and applications more efficiently and effectively. Since fog computing originates from and is a non-trivial extension of cloud computing, it inherits many security and privacy challenges of cloud computing, causing the extensive concerns in the research community. To enable authentic and confidential communications among a group of fog nodes, in this paper, we propose an efficient key exchange protocol based on ciphertext-policy attribute-based encryption (CP-ABE) to establish secure communications among the participants. To achieve confidentiality, authentication, verifiability, and access control, we combine CP-ABE and digital signature techniques. We analyze the efficiency of our protocol in terms of security and performance. We also implement our protocol and compare it with the certificate-based scheme to illustrate its feasibility.

Social networking has become part of our life in recent years, allowing users to converse and connect with people sharing similar interests in real world. However, networking via the social media suffers from serious privacy issues, and one of which is profile attribute leakage in friend discovery. While existing studies mainly focus on leveraging rich cryptographic algorithms to prevent privacy leak, we propose a novel perturbation-based private profile matching mechanism by mixing the private data with random noise to preserve privacy in this paper. In this paper, we consider the case where the profiles are fine-grained, meaning that each attribute is associated with a user-specific numerical value to indicate the level of interest. By carefully tuning the amount of information owned by each party, we guarantee that privacy is effectively preserved while the matching result of users' profiles can be cooperatively obtained. We first give an introduction to a basic scheme, then detail two improved ones by, respectively, taking collusion attack and verifiability into consideration. As no expensive encryption algorithms get involved, our methods are computationally efficient; thus they are more practical for real-world applications. Theoretical security analysis as well as comparison-based simulation studies are carried out to evaluate the performance of our designs.

Authentication plays a critical role in securing any online banking system, and many banks and various services have long relied on username/password combos to verify users. Memorizing usernames and passwords for a lot of accounts becomes a cumbersome and inefficient task. Furthermore, legacy authentication methods have failed over and over, and they are not immune against a wide variety of attacks that can be launched against users, networks, or authentication servers. Over the years, data breach reports emphasize that attackers have created numerous high-tech techniques to steal users' credentials, which can pose a serious threat. In this paper, we propose an efficient and practical user authentication scheme using personal devices that utilize different cryptographic primitives, such as encryption, digital signature, and hashing. The technique benefits from the widespread usage of ubiquitous computing and various intelligent portable and wearable devices that can enable users to execute a secure authentication protocol. Our proposed scheme does not require an authentication server to maintain static username and password tables for identifying and verifying the legitimacy of the login users. It not only is secure against password-related attacks, but also can resist replay attacks, shoulder-surfing attacks, phishing attacks, and data breach incidents.

This paper proposes a multiple-input multiple-output (MIMO) based cooperative dynamic spectrum access (DSA) framework that enables multiple primary users (PUs) and multiple secondary users (SUs) to cooperate in spectrum sharing. By exploiting MIMO in cooperative DSA, SUs can relay the primary traffic and send their own data at the same time, which greatly improves the performance of both PUs and SUs when compared to the non-MIMO time-division spectrum sharing schemes. Especially, we focus on the relay selection optimization problem among multiple PUs and multiple SUs. The network-wide cooperation and competition are formulated as a bargaining game, and an algorithm is developed to derive the optimal PU-SU relay assignment and resource allocation. Evaluation results show that both primary and secondary users achieve significant utility gains with the proposed framework, which gives all of them incentive for cooperation.

Payment systems play a significant role in our daily lives. They are an important driver of economic activities and a vital part of the banking infrastructure of any country. Several current payment systems focus on security and reliability but pay less attention to users’ needs and behaviors. For example, people may share their bankcards with friends or relatives to withdraw money for various reasons. This behavior can lead to a variety of privacy and security issues since the cardholder has to share a bankcard and other sensitive information such as a personal identification number (PIN). In addition, it is commonplace that cardholders may lose their cards, and may not be able to access their accounts due to various reasons. Furthermore, transferring money to an individual who has lost their bankcard and identification information is not a straightforward task. A user-friendly person-to-person payment system is urgently needed to perform secure and reliable transactions that benefit from current technological advancements. In this paper, we propose two secure fund transfer methods termed QuickCash Online and QuickCash Offline to transfer money from peer to peer using the existing banking infrastructure. Our methods provide a convenient way to transfer money quickly, and they do not require using bank cards or any identification card. Unlike other person-to-person payment systems, the proposed methods do not require the receiving entity to have a bank account, or to perform any registration procedure. We implement our QuickCash payment systems and analyze their security strengths and properties.

As a promising paradigm, mobile crowdsensing exerts the potential of widespread sensors embedded in mobile devices. The greedy nature of workers brings the problem of low-quality sensing data, which poses threats to the overall performance of a crowdsensing system. Existing works often tackle this problem with additional function components. In this paper, we systematically formulate the problem into a crowdsensing interaction process between a requestor and a worker, which can be modeled by two types of iterated games with different strategy spaces. Considering that the low-quality data submitted by the workers can reduce the requestor’s payoff and further decrease the global income, we turn to controlling the social welfare in the games. To that aim, we take advantage of zero-determinant strategy, based on which we propose two social welfare control mechanisms under both game models. Specifically, we consider the requestor as the controller of the games and, with proper parameter settings for the to-be-adopted zero-determinant strategy, social welfare can be optimized to the desired level no matter what strategy the worker adopts. Simulation results demonstrate that the requestor can achieve the maximized social welfare and keep it stable by using our proposed mechanisms.

Coverage and connectivity are two important performance evaluation indices for wireless sensor networks (WSNs). In this paper, we focus on the connected target k-coverage (CTC k ) problem in heterogeneous wireless sensor networks (HWSNs). A centralized connected target k-coverage algorithm (CCTC k ) and a distributed connected target k-coverage algorithm (DCTC k ) are proposed so as to generate connected cover sets for energy-efficient connectivity and coverage maintenance. To be specific, our proposed algorithms aim at achieving minimum connected target k-coverage, where each target in the monitored region is covered by at least k active sensor nodes. In addition, these two algorithms strive to minimize the total number of active sensor nodes and guarantee that each sensor node is connected to a sink, such that the sensed data can be forwarded to the sink. Our theoretical analysis and simulation results show that our proposed algorithms outperform a state-of-art connected k-coverage protocol for HWSNs.

Pocket switched networks (PSNs) take advantage of human mobility to deliver data. Investigations on real-world trace data indicate that human mobility shows an obvious spatial regularity: a human being usually visits a few places at high frequencies. These most frequently visited places form the home of a node, which is exploited in this paper to design two HomE based Relay selectiOn (HERO) algorithms. Both algorithms input single data copy into the network at any time. In the basic HERO, only the first node encountered by the source and whose home overlaps a destination’s home is selected as a relay while the enhanced HERO keeps finding more optimal relay that visits the destination’s home with higher probability. The two proposed algorithms only require the relays to exchange the information of their home and/or the visiting frequencies to their home when two nodes meet. As a result, the information update is reduced and there is no global status information that needs to be maintained. This causes light loads on relays because of the low communication cost and storage requirements. Additionally, only simple operations are needed in the two proposed algorithms, resulting in little computation overhead at relays. At last, a theoretical analysis is performed on some key metrics and then the real-world based simulations indicate that the two HERO algorithms are efficient and effective through employing only one or a few relays.

Considering the energy constraint for fixed sensor nodes and the unacceptable long propagation delay, especially for latency sensitive applications of underwater acoustic sensor networks, we propose a MAC protocol that is latency-optimized and energy-efficient scheme and combines the physical layer and the MAC layer to shorten transmission delay. On physical layer, we apply convolution coding and interleaver for transmitted information. Moreover, dynamic code rate is exploited at the receiver side to accelerate data reception rate. On MAC layer, unfixed frame length scheme is applied to reduce transmission delay, and to ensure the data successful transmission rate at the same time. Furthermore, we propose a network topology: an underwater acoustic sensor network with mobile agent. Through fully utilizing the supper capabilities on computation and mobility of autonomous underwater vehicles, the energy consumption for fixed sensor nodes can be extremely reduced, so that the lifetime of networks is extended.