Large Language Models (LLMs) are widely used in high-stakes applications, making their interpretability increasingly important.Existing interpretability methods are typically categorized into internal and external perspectives, which are often studied in isolation and tend to overlook two key aspects: causality and temporal dynamics.Explanations are often limited to surface correlations or static dependencies, failing to capture how influences evolve during autoregressive generation.To address these limitations, we propose a causal and dynamic interpretability framework for LLM generation.We first characterize the backdoor-adjusted causal effects of both the generated prefix and the prompt on the current token using the Structural Causal Model.Next, we introduce two metrics to quantify contextual causal influence and question-answer causal influence.Overall, our work provides a unified causal view of internal consistency and external alignment in LLM generation dynamics.1

The crux of data disclosure lies in the meticulous quantification and judicious trade-off between privacy leakage and utility. Firstly, the measurement of privacy leakage is the premise of sensitive data compliance disclosure. Existing solutions are mainly based on the qualitative perspective and the group perspective, which are unable to quantitatively measure the risk of individual privacy leakage. Secondly, in terms of balancing privacy leakage and utility, existing solutions overlook uninformed disclosure scenarios. In such scenarios, the two-dimensional privacy-utility game will be reduced to the optimization of a single parameter of mutual information. To address the aforementioned drawbacks, this paper proposes a data disclosure mechanism tailored for heterogeneous privacy profiles. Specifically, we decouple the multilevel privacy leakage through mutual information. By respectively addressing the informed and uninformed disclosure, we achieve the optimization of the joint potential energy surface of privacy and utility, breaking through the dilemma of excessive protection and utility collapse in data disclosure. On this basis, we conduct a comprehensive discussion on the two data disclosure strategies, namely introducing disturbance and linear obfuscation. The results of extensive simulations verify the effectiveness of the proposed mechanism. Finally, our work shows that balancing privacy and utility in data disclosure is unfeasible with disturbance-introducing strategies. In contrast, using linear obfuscation strategies can achieve such a balance, and the optimal approach is the disclosure scheme that minimizes the data disclosure loss.

Bitcoin has witnessed a prevailing transition that employing transaction fees paid by users rather than subsidy assigned by the system as the main incentive for mining. The adjustability of reward in the transaction fee-incentivized regime makes room for the mining gap, a period of time in which miners turn mining rigs off until transaction fees are sufficient. Obviously, the mining gap aggressively weakens the security of Bitcoin, and is further extended by the selfishness of rational users who tend to provide low transaction fees. The phenomena of mining gap traps Bitcoin system into the egoistic dilemma which is a challenging problem since it involves games not only between users and miners bilaterally, but also among miners and users internally. Hence, in this paper, we first derive the property of strategic complementarity among users/miners, which enables us to reasonably untangle the antagonism among the homogenous players. Based on this, an incentive mechanism leveraging the zero-determinant theory is designed to get rid of the dilemma. To the best of our knowledge, this paper is the first work to cap the mining gap and solve the egoistic dilemma in Bitcoin. Both theoretical analyses and numerical simulations demonstrate the effectiveness of our proposed mechanism.

The feverish personal data gold rush has made sensitive information leakage a non-negligible issue, turning it into a popular target for malicious attacks. Although some data may not seem to reveal private information directly, they could still be exploited through malicious intervention and inference; such data are referred to as risk data. Studies have claimed that such risk data exhibit the trait ofmacro-level collaborative leakage, meaning that individually harmless risk data can reveal sensitive information when combined. However, why the macro-level collaborative leakage will occur remains relatively uncharted. Hence, in this paper, we conduct rigorous quantitative analyses for the first time, to trace the root of the macro-level collaborative leakage. We conclude that this phenomenon arises from thecollaborative effectsamong pieces of risk data concerning sensitive information. In light of this, we formulate the sufficient condition for the occurrence of the macro-level collaborative leakage and investigate its presence in the Gaussian-distributed data. We highlight that, on the one hand, the Gaussian distribution can align the correlation between risk data and sensitive data at both the micro- and macro- levels, thereby preventing the macro-level collaborative leakage. This reveals the potential of the Gaussian distribution in enhancing data privacy protection from a macro perspective. On the other hand, the ability of the Gaussian distribution to counteract the macro-level collaborative leakage is inherently limited, which further corroborates the ubiquity of this phenomenon. Our insights underscore the need for more comprehensive security and privacy protection mechanisms to ensure data security and confidentiality.

Public key authenticated encryption with keyword search (PAEKS) has been widely studied in cloud storage systems, which allows the cloud server to search encrypted data while safeguarding against insider keyword guessing attacks (IKGAs). Most PAEKS schemes are based on the discrete logarithm (DL) hardness. However, this assumption becomes insecure when it comes to quantum attacks. To address this concern, there have been studies on post-quantum PAEKS based on lattice. But to our best knowledge, current lattice-based PAEKS exhibit limited applicability and security, such as only supporting single user scenarios, or encountering secret key leakage problem. In this paper, we propose FS-MUAEKS, the forward-secure multi-user authenticated searchable encryption, mitigating the secret key exposure problem and further supporting multi-user scenarios in a quantum setting. Additionally, we formalize the security models of FS-MUAEKS and prove its security in the random oracle model (ROM). Ultimately, the comprehensive performance evaluation indicates that our scheme is computationally efficient and surpasses other state-of-the-art PAEKS schemes. The ciphertext generation overhead of our scheme is only 0.27 times of others in the best case. The communication overhead of our FS-MUAEKS algorithm is constant at 1.75MB under different security parameter settings.

The natural forking severely compromises the security and wastes resources of Blockchain. Current analyses of the natural forking are carried out from microscale and macroscale perspectives, each facing challenges in generality and accuracy respectively. This results in dire straits that the forking arising from the network layer cannot be solved within the same layer, and existing defense schemes concentrate on the consensus layer, unfortunately coming at the expense of diminishing decentralized trust. Hence, to overcome these shortcomings, we propose the first reconnaissance-strike solution to the natural forking where the issue is recognized at the network layer and further struck on-site. Specifically, our endeavors encompass 1) analyzing the spatial-temporal transmission dynamics of the main chain. We exert mesoscale perspective by transforming the behavioral analysis of the transmitters (i.e., the nodes) into the movement analysis of the transmitted object (i.e., the main chain) which mitigates following Lévy mobility. Based on this, we quantify the dynamics of long-range leaping and short-range diffusion of the main chain transmission; 2) proposing a cost-effective anti-forking mechanism. This mechanism combats the forking with low cost by configuring logical connections at the network management level, based on the quantitative relationship between Blockchain network topology and the natural forking rate we have derived. Both theoretical analysis and extensive experiments verify that our scheme can maintain the natural forking rate not more than the given threshold in most cases.

The way of task posting serves as the main pillar in achieving an efficient crowdsourcing market. Pioneer solutions on task posting can be categorized as retail task posting and batch task posting. Unlike retail task posting, which simply matches the most suitable worker to tasks, batch task posting considers the collaborations not only between workers and tasks but also among tasks, which brings high efficiency, low costs, and satisfactory task completion rates. However, the state of the arts on batch task posting leverage specific attributes to combine tasks as bundles for posting, leading to limited scalability. Hence, we propose a causal analysis framework for batch crowdsourcing to achieve an attribute-independent batch crowdsourcing solution that disentangles multi-factors to uncover the posting merits of tasks bundled at optimal prices, based on which an approximately optimal algorithm is further introduced to form reasonable bundles for posting. Since batch crowdsourcing may incur losses due to short-term profit fluctuation, a risk assessment method is proposed to encourage the requestor to act properly for loss mitigations. Our work explores the causal analysis and risk assessment in batch crowdsourcing for the first time, with the following highlights: 1)generality. It proposes a composite metric for gauging task bundles which avoids the issue of attribute dependence in the state of the arts, resulting in better universality; 2)synergy. By collaboratively considering the “value” and “relative position” of variables, our work derives results reflecting causal relationships rather than naive correlations; and 3)precision. We not only elucidate the probability of risk in batch crowdsourcing but also delineate the rate function governing its probability decay. This allows a requestor to know when and how fast to halt batch task posting.

The integration of directed acyclic graph (DAG)-based blockchain and Internet of Things (IoT) aims at improving the efficiency of data storage. However, if massive IoT data are not placed in an organized way, the search and usage of the data for upper-level applications can be burdensome, since they have to examine the data block by block, which also increases the difficulty of data verification, affecting consensus efficiency. To maintain the high throughput advantage of DAG-based blockchain applied in IoT and improve the data analysis efficiency, we propose a novel consensus mechanism named TidyBlock, including the transaction collation mechanism for block generation and the block selection mechanism for verification. The first mechanism can tidy up scattered transactions before they are packaged into blocks, while the second one can collate blocks to facilitate verification, realizing a two-layer collation of IoT data so as to increase analysis efficiency of upper-level IoT applications. Additionally, the second mechanism can provide a self-driven incentive for rational participants to follow the first one in case they are reluctant to do extra collation work. Theoretical analysis is provided to demonstrate the validity of our proposed algorithms by formal methods. Extensive simulations based on synthetic data verify the rationality and effectiveness of the proposed mechanisms.

Game theory is an effective analytical tool for crowdsourcing. Existing studies based on it share a commonality: the influence of players’ decisions isbilateral. However, the status is broken by the zero-determinant (ZD) strategy, where the ZD player canunilaterallycontrol the opponent's expected payoff. Thereby, crowdsourcing games trigger conclusions that differ from traditional ones. By addressing three questions, this paper is the first work to analyze the turbulence in crowdsourcing caused by the inequality between the requestor and the worker in the ZD game. The first question reveals the potential for the requestor to exploit the worker; the second question quantifies the worker's tolerance towards exploitation, providing a basis for confrontation; the third question serves as the cornerstone for maintaining the crowdsourcing, regulating the requestor's exploitative behavior. To answer these questions, we extend ZD strategies from binary games to continuous ones, not only revealing the requestor's dominance but also enriching the theoretical system of ZD strategies and broadening their application. Furthermore, we introduce the worker's dissatisfaction degree, identifying the exponential trend and decay rate, revealing optimal timing and speed for the worker's effective confrontation and maximum exploitation for the requestor. Numerical simulations have validated the effectiveness of our analyses.

The global maritime trade plays a key role in propagating alien aquatic invasive species, which incurs side effects in terms of environment, human health and economy. The existing biosecurity methods did not take into account the invaded risk as well as the diffusion of invasive species at the same time, which may lead to inadequate bioinvasion control. In addition, the lack of considering the impact of bioinvasion control on shipping also makes their methods cost-ineffective. To solve the problems of the existing methods, we employ the AIS data, the ballast water data and the water temperature & salinity data to construct two networks: the species invasion network (SIN) and the global shipping network (GSN). The former is used to analyze the potential of a port in propagating marine invasive species while the latter is employed to evaluate the shipping importance of ports. Based on the analysis of SIN and GSN, two categories of biosecurity triggering mechanisms are proposed. The first category takes into consideration both being bioinvaded and spreading invasive species and the second one concerns the shipping value of each port besides its invasion risk. A lot of case studies have been done to discover the key ports needed to be controlled preferentially under the guide of the proposed biosecurity triggering mechanisms. Finally, our correlation analysis shows that closeness is most highly correlated to the invasion risk.

Mobile networks are facing unprecedented challenges due to the traits of large scale, heterogeneity, and high mobility. Fortunately, the emergence of fog computing offers surprisingly perfect solutions considering the features of consumer proximity, wide-spread geographical distribution, and elastic resource sharing. In this paper, we propose a novel mobile networking framework based on fog computing which outperforms others in resilience. Our scheme is constituted of two parts: the personalized customization mobility management (MM) and the market-driven resource management (RM). The former provides a dynamically customized MM framework for any specific mobile node to optimize the handoff performance according to its traffic and mobility traits; the latter makes room for economic tussles to find out the competitive service providers offering a high level of service quality at sound prices. Synergistically, our proposed MM and RM schemes can holistically support a full-fledged resilient mobile network, which has been practically corroborated by numerical experiments.

Sharing the big data generated by IoT via cloud is slow and expensive. Besides, transmitting and sharing data among IoT devices via cloud may be insecure. To address these issues, a novel efficient and secure data sharing scheme termed EB-SDSS (Edge Blockchain Secure Data Sharing Scheme) is proposed in this paper for edge-enabled IoT applications. EB-SDSS constructs a blockchain on edge servers. It guarantees the confidentiality and unforgeability of data by combining the symmetric encryption scheme with an edge blockchain. To ensure the device authenticity and the reliability of shared data, EB-SDSS adopts a certificateless signature scheme. It also provides efficient large-scale data searches for IoT devices through a locality-sensitive hashing algorithm. EB-SDSS has been proven to be secure against the adaptive chosen message attacks under the random oracle model. The experimental results indicate that EB-SDSS is feasible for IoT inter-device data sharing.

Overprivilege Attack, a widely reported phenomenon in IoT that accesses unauthorized or excessive resources, is notoriously hard to prevent, trace and mitigate. In this paper, we propose TBAC, a Tokoin-Based Access Control model enabled by blockchain and Trusted Execution Environment (TEE) technologies, to offer fine-grained access control and strong auditability for IoT. TBAC materializes the virtual access power into a definite-amount, secure and accountable cryptographic coin, termed “tokoin” (token+coin), and manages it using atomic and accountable state-transition functions in a blockchain. A tokoin carries a fine-grained policy defined by the resource owner to specify the requirements to be satisfied before an access is granted, and the behavioral constraints that describe the correct procedure to follow during access. The strong-auditability is achieved with blockchain and a TEE-enabled trusted access control object (TACO) to ensure that all access activities are securely monitored and auditable. We prototype TBAC by implementing all its functions with well-studied cryptographic primitives over different blockchain platforms, building a TACO on top of the ARM Cortex-M33 TEE microcontroller, and constructing a user-friendly APP for regular users. A case study is finally presented to demonstrate how TBAC is employed to enable autonomous and secure in-home cargo delivery.

In the current hypergrowth phase of the Internet of Things, cross-domain data access becomes more and more frequently. Whereas, the lack of trust between domains makes cross-domain data access extremely hard. Traditional schemes typically depend on a third party to establish trust between data accessing entities, which can easily result in single point of failure. To conquer the aforementioned challenge, this paper proposes BSCDA, a blockchain-based cross-domain data access scheme designed to enable secure data transmission across domains. The decentralization, transparency, and anti-tampering features of blockchain perfectly solve the issue of single point of failure and foster trust among various domains. Specifically, a certificate management method is developed to address the certificate storage issue by leveraging a mapping table to store the revocation certificate index on the blockchain. This method not only ensures the verifiability of the certificate but also reduces the storage overhead. Additionally, a four-party key agreement mechanism is designed to guarantee the secure data transmission during the process of cross-domain data access. Security analysis prove the feasibility of our proposed scheme. Extensive experiments demonstrate the superiority of our scheme in cross-domain data access.

Decentralized storage is recognized as a promising blockchain application for building trustworthy data-sharing services. Recently, privacy-preserving decentralized storage has attracted tremendous attention from the research community, due to the inherent transparent access properties of blockchain. That is, multi-users can search over encrypted data from multi-owners via untrusted storage servers. Private Set Intersection (PSI) is regarded as an ideal cryptographic scheme for this scenario because it allows multiple parties to collaboratively execute private set computations without revealing additional information. However, existing solutions do not consider the necessary verification and fault tolerance of PSI results, which is the indispensable security requirements in decentralized storage. To fill the gap, in this work, we introduce the first reliable PSI scheme for decentralized storage that provides results verifiability and fault tolerance for private set operations. Our design leverages authenticated indexing structures and Shamir's secret sharing algorithm for constructing our reliable PSI scheme with fault tolerance. To address the potential malicious behaviors of dishonest users or servers, we also propose a blockchain-assisted arbitration protocol that enables public arbitration in a privacy-preserving manner. We rigorously provide security analysis and complete the prototype implementation on Fabric. Extensive results demonstrate its practicability and feasibility for existing decentralized storage.

Although proof of work (PoW) consensus dominates the current blockchain-based systems mostly, it has always been criticized for the uneconomic brute-force calculation. As alternatives, energy-conservation and energy-recycling mechanisms heaved in sight. In this paper, we propose proof of user similarity (PoUS), a distinct energy-recycling consensus mechanism, harnessing the valuable computing power to calculate the similarities of users, and enact the calculation results into the packing rule. However, the expensive calculation required in PoUS challenges miners in participating, and may induce plagiarism and lying risks. To resolve these issues, PoUS embraces thebest-effortschema by allowing miners to compute partially. Besides, a voting mechanism based on the secure two-party computation and Bayesian truth serum is proposed to guarantee privacy-preserved voting and truthful reports. Noticeably, PoUS distinguishes itself in recycling the computing powerback to blockchainsince it turns the resource wastage to facilitate refined cohort analysis of users, serving as thespatial measurerand enabling asearchableblockchain. We build a prototype of PoUS and compare its performance with PoW. The results show that PoUS outperforms PoW in achieving an average transaction per second (TPS) improvement of 24.01% and an average confirmation latency reduction of 43.64%. Besides, PoUS functions well in mirroring the spatial information of users, with negligible computation time and communication cost.

Fog computing is one of the promising models for mobile edge computing, and greatly reduces the latency of applications by establishing a distributed fog caching system on IoT devices. However, with the popularity and application of fog computing, there are growing concerns about the integrity and security of cached data. Different from centralized cloud servers, fog nodes are distributed and have limited computing and communication capabilities. Therefore, the direct adoptions of the existing centralized data integrity assurance schemes would incur significant communication overheads and cannot meet the real-time requirements of the fog-based applications. In this article, we propose an efficient and secure collaborative cached data auditing scheme for distributed fog computing. The proposed scheme, named FogAudit, enables untrusted fog nodes to collaboratively provide caching services while protecting the integrity and security of cached data. Our design can efficiently support fog nodes to collaborate with each other to identify malicious nodes and realize data recovery without relying on a centralized authority. Besides, we devise ESV, a tailored auditing protocol based on distributed consensus mechanism to handle disputes in the process of collaborative auditing. We provide a formal security analysis and experimentally evaluate its performance against three representative auditing schemes. Our security analysis and experimental evaluation results confirm that FogAudit is efficient and secure.

Natural forking is tantamount to the “black swan” event in blockchain since it emerges unexpectedly with a small probability, and may incur low resource utilization and costly economic loss. The ongoing literature analyzes natural forking mainly from the macroscopic perspective, which is insufficient to further understand this phenomenon since it roots in theinstantaneous differencebetween block creation and propagation microscopically. Hence, in this article, we fill this gap by leveraging the large deviation theory to conduct the first micro study of natural forking, aiming to reveal its inherent mechanism substantially. Our work is featured by 1)conceptual innovation. We creatively abstract the blockchain overlay network as a “service system”. This allows us to investigate natural forking from the perspective of “supply and demand”. Based on this, we can identify the competitive dynamics of blockchain and construct a queuing model to characterize natural forking; 2)progressiveness. We scrutinize the natural forking probability as well as its decay rate via a three-step scheme from simple to complex, which are the single-source i.i.d. scheme, the single-source non-i.i.d. scheme, and the many-source non-i.i.d. scheme. By doing so, we can answerwhenandhow fastshould we take actions andwhatactions should we take against natural forking. Our valuable findings can not only put forward decisive guidelines theoretically from the top level, but also engineer optimal countermeasures operationally on a practical level to thwart natural forking.

Online social networks revolutionize the way people interact with each other. When various social network information aggregates over time, a rich online profile of the user is formed. Owing to the various features provided by mobile devices, a user's online social activities are tightly tied to his phone, and are conveniently, sometimes unnecessarily, available to social networks. In this article, we propose a novel attack architecture to show that attackers can infer a user's social network identities behind a mobile device through new dimensions. Specifically, we first developed a correlation between a user's device system states and the social network events, which leverage multiple mechanisms such as the learning-based memory regression model, to infer the possible accounts of the user in the social network app. Then we exploited the social network to social network correlation, via which we correlated information across different social networks, to identify the accounts of the target user. We implemented and evaluated these attacks on three popular social networks, and the results corroborate the effectiveness of our design.

The explosive amount of data generated at the network edge makes mobile edge computing an essential technology to support real-time applications, calling for powerful data processing and analysis provided by machine learning (ML) techniques. In particular, federated edge learning (FEL) becomes prominent in securing the privacy of data owners by keeping the data locally used to train ML models. Existing studies on FEL either utilize in-process optimization or remove unqualified participants in advance. In this paper, we enhance the collaboration from all edge devices in FEL to guarantee that the ML model is trained using all available local data to accelerate the learning process. To that aim, we propose acollective extortion (CE)strategy under the imperfect-information multi-player FEL game, which is proved to be effective in helping the server efficiently elicit the full contribution of all devices without worrying about suffering from any economic loss. Technically, our proposed CE strategy extends the classical extortion strategy in controlling the proportionate share of expected utilities for a single opponent to the swiftly homogeneous control over a group of players, which further presents an attractive trait of being impartial for all participants. Moreover, the CE strategy enriches the game theory hierarchy, facilitating a wider application scope of the extortion strategy. Both theoretical analysis and experimental evaluations validate the effectiveness and fairness of our proposed scheme.

Though voting-based consensus algorithms in blockchain outperform proof-based ones in energy- and transaction-efficiency, they are prone to incur wrong elections and bribery elections. The former originates from the uncertainties of candidates’ capability and availability, and the latter comes from the egoism of voters and candidates. Hence, in this paper, we propose an uncertainty- and collusion-proof voting consensus mechanism, including the selection pressure-based voting algorithm and the trustworthiness evaluation algorithm. The first algorithm can decrease the side effects of candidates’ uncertainties, lowering wrong elections while trading off the balance between efficiency and fairness in voting miners. The second algorithm adopts an incentive-compatible scoring rule to evaluate the trustworthiness of voting, motivating voters to report true beliefs on candidates by making egoism consistent with altruism so as to avoid bribery elections. A salient feature of our work is theoretically analyzing the proposed voting consensus mechanism by the large deviation theory. Our analysis provides not only the voting failure rate of a candidate but also its decay speed. The voting failure rate measures the incompetence of any candidate from a personal perspective by voting, based on which the concepts of the effective selection valve and the effective expectation of merit are introduced to help the system designer determine the optimal voting standard and guide a candidate to behave in an optimal way for lowering the voting failure rate.

Blockchain creates a secure environment on top of strict cryptographic assumptions and rigorous security proofs. It permits on-chain interactions to achieve trustworthy properties such as traceability, transparency, and accountability. However, current blockchain trustworthiness is only confined to on-chain, creating a “trust gap” to the physical, off-chain environment. This is due to the lack of a scheme that can truthfully reflect the physical world in a real-time and consistent manner. Such an absence hinders further blockchain applications in the physical world, especially for the security-sensitive ones. In this paper, we propose a framework to extend blockchain trust from on-chain to off-chain, and take trustworthy vaccine tracing as an example scheme. Our scheme consists of 1) a Trusted Execution Environment (TEE)-enabled trusted environment monitoring system built with the Arm Cortex-M33 microcontroller that continuously senses the inside of a vaccine box through trusted sensors and generates anti-forgery data; and 2) a consistency protocol to upload the environment status data from the TEE system to blockchain in a truthful, real-time consistent, continuous and fault-tolerant fashion. Our security analysis indicates that no adversary can tamper with the vaccine in any way without being captured. We carry out an experiment to record the internal status of a vaccine shipping box during transportation, and the results indicate that the proposed system incurs an average latency of 84 ms in local sensing and processing followed by an average latency of 130 ms to have the sensed data transmitted to and been available in the blockchain.

Quality control plays a critical role in crowdsourcing. The state-of-the-art work is not suitable for crowdsourcing applications that require extensive validation of the tasks quality, since it is a long haul for the requestor to verify task quality or select professional workers in a one-by-one mode. In this paper, we propose a misreport- and collusion-proof crowdsourcing mechanism, guiding workers to truthfully report the quality of submitted tasks without collusion by designing a mechanism, so that workers have to act the way the requestor would like. In detail, the mechanism proposed by the requester makes no room for the workers to obtain profit through quality misreport and collusion, and thus, the quality can be controlled without any verification. Extensive simulation results verify the effectiveness of the proposed mechanism. Finally, the importance and originality of our work lie in that it reveals some interesting and even counterintuitive findings: 1) a high-quality worker may pretend to be a low-quality one; 2) the rise of task quality from high-quality workers may not result in the increased utility of the requestor; 3) the utility of the requestor may not get improved with the increasing number of workers. These findings can boost forward looking and strategic planning solutions for crowdsourcing.

The pool-hopping attack casts down the expected profits of both the mining pool and honest miners in Blockchain. The mainstream countermeasures, namely PPS (pay-per-share) and PPLNS (pay-per-last-N-share), can hedge pool hopping but need to charge miners some fees when they join in a pool. Obviously, the higher fee charged, the higher cost of joining the pool, the less motivation of a miner to mine in the pool. In this article, we apply the zero-determinant (ZD) theory to design a novel pooled mining which offers an incentive mechanism for motivating miners not to switch in pools strategically by economic means without fee charged. In short, the proposed pooled mining has three unique features: 1) fee-free. No fee is charged if the miner does not hop, 2) wide applicability. It can be employed in both prepaid and postpaid mechanisms, and 3) fairness. Even can dominate the game with any miner, a pool has to cooperate when a miner does not hop among pools, implying that the pool cannot squeeze the honest miners financially. The fairness of our scheme makes it have long-term sustainability. Both theoretical analyses and numerical simulations demonstrate the effectiveness of our scheme.

The growing threat of personal data breach in data trading pinpoints an urgent need to develop countermeasures for preserving individual privacy. The state-of-the-art work either endows the data collector with the responsibility of data privacy or reports only a privacy-preserving version of the data. The basic assumption of the former approach that the data collector is trustworthy does not always hold true in reality, whereas the latter approach reduces the value of data. In this paper, we investigate the privacy leakage issue from the root source. Specifically, we take a fresh look to reverse the inferior position of the data provider by making her dominate the game with the collector to solve the dilemma in data trading. To that aim, we propose the noisy-sequentially zero-determinant (NSZD) strategies by tailoring the classical zero-determinant strategies, originally designed for the simultaneous-move game, to adapt to the noisy sequential game. NSZD strategies can empower the data provider to unilaterally set the expected payoff of the data collector or enforce a positive relationship between her and the data collector's expected payoffs. Both strategies can stimulate a rational data collector to behave honestly, boosting a healthy data trading market. Numerical simulations are used to examine the impacts of key parameters and the feasible region where the data provider can be an NSZD player. Finally, we prove that the data collector cannot employ NSZD to further dominate the data market for deteriorating privacy leakage.

A cost-efficient deal that can achieve high sensing quality with a low reward is the permanent goal of the requestor in mobile crowdsensing, which heavily depends on the quantity and quality of the workers. However, the spatial diversity and temporal dynamics lead to heterogeneous worker supplies, making it hard for the requestor to utilize a homogeneous pricing strategy to realize a cost-efficient deal from a systematic point of view. Therefore, a cost-efficient deal calls for a cost-efficient pricing strategy, boosting the whole sensing quality with less operation (computation) cost. However, the state-of-the-art studies ignore the dual cost-efficient demands of large-scale sensing tasks. Hence, we propose a combinatorial pinning zero-determinant (ZD) strategy, which empowers the requestor to utilize a single strategy within its feasible range to minimize the total expected utilities of the workers throughout all sensing regions for each time interval, without being affected by the strategies of the workers. Through turning the worker-customized strategy to an interval-customized one, the proposed combinatorial pinning ZD strategy reduces the number of pricing strategies required by the requestor from O(n3) to O(n). Besides, it extends the application scenarios of the classical ZD strategy from two-player simultaneous-move games to multiple-heterogeneous-player sequential-move ones, where a leader can determine the linear relationship of the players' expected utilities. Such an extension enriches the theoretical hierarchy of ZD strategies, broadening their application scope. Extensive simulations based on real-world data verify the effectiveness and efficiency of the proposed scheme.

Proof of work (PoW), the most popular consensus mechanism for blockchain, requires ridiculously large amounts of energy but without any useful outcome beyond determining accounting rights among miners. To tackle the drawback of PoW, we propose a novel energy-recycling consensus algorithm, namely proof of federated learning (PoFL), where the energy originally wasted to solve difficult but meaningless puzzles in PoW is reinvested to federated learning. Federated learning and pooled-mining, a trend of PoW, have a natural fit in terms of organization structure. However, the separation between the data usufruct and ownership in blockchain lead to data privacy leakage in model training and verification, deviating from the original intention of federal learning. To address the challenge, a reverse game-based data trading mechanism and a privacy-preserving model verification mechanism are proposed. The former can guard against training data leakage while the latter verifies the accuracy of a trained model with privacy preservation of the task requester's test data as well as the pool's submitted model. To the best of our knowledge, our article is the first work to employ federal learning as the proof of work for blockchain. Extensive simulations based on synthetic and real-world data demonstrate the effectiveness and efficiency of our proposed mechanisms.

The Internet of Things (IoT) changed our lives with huge amounts of data production. Due to source-limited IoT devices, one of the best ways to process the data is cloud storage. However, a series of security and privacy issues arise, such as illegal data access, data tampering, and privacy leak. Though symmetric encryption can guarantee data confidentiality, it cannot realize fine-grained data sharing and searching. The keyword-based searchable attribute-based encryption (KSABE) can achieve data confidentiality and fine-grained access control. More importantly, it realizes a keyword-based search for data users. However, the heavy decryption computation burden and the management of massive user keys appear when implementing attribute-based encryption schemes to IoT. Therefore, this article proposes a blockchain-aided searchable attribute-based encryption (BC-SABE) with efficient revocation and decryption, where the traditional centralized server is replaced with a decentralized blockchain system being in charge of the threshold parameter generation, key management, and user revocation. All revocation tasks are done by the blockchain and it is on longer necessary for ciphertext reencryption and key update. Moreover, users utilize the coalition blockchain to generate partial tokens. Besides, the cloud server contained in our scheme not only stores the massive encrypted data but also performs search and predecryption for users who only require one exponentiation in the group G to decrypt fully. Security analyses prove that our scheme realizes the security under the chosen plaintext attack and the chosen keyword attack. Simulations show that the decryption and token generation cost of our scheme are preferable.

There is evidence that blockchain plays a crucial role in the Internet of Things (IoT)-based implementation due to its transparency, traceability, and immutability, in which the participants are incentivized to behave authentically and precisely for rewards. Despite the domination of subsidy in reward, the decrease of the mining rate and the imperativeness of fees make the fee market become a pivotal role to motivate miners in the blockchain. However, the current mechanism for selecting transactions into a block poses a risk to the stability of the system, which stems from the vicious competition of users and the insufficient incentives of miners. In this article, we propose a novel transaction selection mechanism by leveraging the Lyapunov optimization and large deviation theory. This article is: 1) fair because the proposed mechanism is not single-factor dominated, both personal utility of the miner and overall utility of the system are taken into account; 2) sustainable since miners are incentivized greatly to guarantee the mining behavior; and 3) robust. The analysis based on the large deviation theory enhances the robustness of the blockchain. To the best of our knowledge, we are the first to consider both miner's benefit as well as system benefit to establish a better fee market in the blockchain for IoT enhancement. Our theoretical analyses and simulation results demonstrate the effectiveness of the proposed mechanism.

At present, the proliferation of the online connected devices conceives the Internet of Things (IoT), in which many wireless sensors, smart devices are implemented. However, the nature of openness rooted in IoT makes itself vulnerable to be attacked. One of the pioneer countermeasures is the moving target defense (MTD), which encourages an active and dynamic defense in IoT. In this article, a macroscopic research in MTD is carried out. The existing macroscopic studies take advantage of a traditional game theory. Consequently, protected IoT devices need extra operations to dominate the game. In this article, we take a dramatically different approach where a player can dominate the game without extra operation. Our approach benefits from the power of the zero-determinant (ZD) strategy, in which the player who adopts ZD can unilaterally set the expected payoff of the adversary or itself. Aware of such a powerful strategy, both players may want to employ it for dominating the confrontation. In this case, two fundamental questions need to be answered: who should take the ZD strategy? And to what extent can the ZD player dominate the game? To solve these problems, we model the interactions between the IoT devices and the malicious attackers as a Markov game. Besides, we obtain the conditions to adopt ZD, based on which we deduce the effectiveness of the ZD player. To the best of our knowledge, we are the first to employ the ZD strategy theory to enhance a better counterattack performance in IoT.

One of the best ways to deal with the massive data generated by the Internet of Things (IoT) is storing them in the cloud. However, outsourced storage raises some security and privacy issues, such as data leaking and illegal access. The attribute-based signcryption (ABSC) is one of the most promising approaches which can ensure the confidentiality and authenticity of data simultaneously. Nonetheless, it not only inherits the fine-grained access control but also the heavy computational cost which is intolerable for most resource-limited IoT devices. In this article, we propose lightweight hybrid-policy ABSC (LH-ABSC), a lightweight ABSC scheme which adopts ciphertext-policy encryption (CPABE) and key-policy attribute-based signature (KPABS). Ciphertext-policy attribute-based signature leads the decision making that who can decrypt to the data owners directly. Meanwhile, the signature is related with data owners' attribute set which can be used to testify the authenticity of data. In particular, LH-ABSC has constant signature size and satisfies public verification which is deeply important for IoT devices. Moreover, LH-ABSC outsources most computing overhead to fog nodes, including signature, verify, and decryption. Comprehensive theoretical analyses, such as confidentiality, unforgeability, and verifiability, are provided. Also, the selective chosen ciphertext security, the selective chosen message security, and signers anonymity are achieved.

Crowdsourcing is a promising technology to accomplish a complex task via eliciting services from a large group of contributors. Recent observations indicate that the success of crowdsourcing has been threatened by the malicious behaviors of the contributors. In this paper, we analyze the attack problem using an iterated prisoner's dilemma (IPD) game and propose a reward-penalty expected payoff algorithm based on zero-determinant (ZD) strategies to reward a worker's cooperation or penalize its defection in order to entice the final cooperation. Both theoretical analysis and simulation studies are performed, and the results indicate that the proposed algorithm has the following two attractive characteristics: 1) the requestor can incentivize the worker to become cooperative without any long-term extra cost; and 2) the proposed algorithm is fair so that the requestor cannot arbitrarily penalize an innocent worker to increase its payoff even though it can dominate the game. To the best of our knowledge, we are the first to adopt the ZD strategies to stimulate both players to cooperate in an IPD game. Moreover, our proposed algorithm is not restricted to solve only the problem of crowdsourcing dilemma - it can be employed to tackle any problem that can be formulated into an IPD game.

Quality control in crowdsourcing is challenging due to the heterogeneous nature of the workers. The state-of-the-art solutions attempt to address the issue from the technical perspective, which may be costly because they function as an additional procedure in crowdsourcing. In this paper, an economics based idea is adopted to embed quality control into the crowdsourcing process, where the requestor can take advantage of the market power to stimulate the workers for submitting high-quality jobs. Specifically, we employ two sequential games to model the interactions between the requestor and the workers, with one considering binary strategies while the other taking continuous strategies. Accordingly, two incentive algorithms for improving the job quality are proposed to tackle the sequential crowdsourcing dilemma problem. Both algorithms are based on a sequential zero-determinant (ZD) strategy modified from the classical ZD strategy. Such a revision not only provides a theoretical basis for designing our incentive algorithms, but also enlarges the application space of the classical ZD strategy itself. Our incentive algorithms have the following desired features: 1) they do not depend on any specific crowdsourcing scenario; 2) they leverage economics theory to train the workers to behave nicely for better job quality instead of filtering out the unprofessional workers; 3) no extra costs are incurred in a long run of crowdsourcing; and 4) fairness is realized as even the requestor (the ZD player), who dominates the game, cannot increase her utility by arbitrarily penalizing any innocent worker.

Peer-to-peer (P2P) services such as mobile P2P transmissions and resource sharing, provide efficient methods to deliver data without the deployment of any central server. Nevertheless, the free-riding phenomenon inherit in such services presses a need for incentive mechanisms to stimulate contributions of data transmissions or sharing. As a result, it is imperative to answer the following questions: whether, and if so to what extent, an incentive mechanism can invoke such contributions? To answerthese questions, we employ an n-player continuous quantum game model to analyze the general extrinsic incentive mechanisms as well as the reputation-based incentive mechanisms, a typical class of extrinsic incentive mechanisms. We focus on studying the extrinsic incentive mechanisms in this paper due to their wide scope of applications stemming from the fact that they promote cooperative behaviors by offering rewards rather than depending on the internal bounds (e.g., social ties) among peers, which may not always exist between any pair of peers. To the best of our knowledge, we are the first to analyze the extrinsic incentive mechanisms for P2P services from a quantum game perspective. Such a perspective is adopted because the extended strategy space in the quantum game broadens the range for searching optimal strategies and the introduction of entanglement makes the proposed analytical frameworks more practical due to the consideration of the peers' relationships imposed by the rewards in extrinsic incentive mechanisms. Our quantum game-based analytical framework is generic because it is compatible with classic game-based schemes. The analytical results can provide a straightforward insight on evaluating the potential of the extrinsic incentive mechanisms and can serve as important references for designing new extrinsic incentive mechanisms.

The extreme boom of online social networks paves the way for rumor propagation, which may incur an economic loss and cause further public panic. Hence, there is a pressing need to develop countermeasures for reducing side effects posed by rumors. Different from the state-of-the-art work that mostly conducted micro-perspective studies, our paper focuses on a macro-perspective one. In detail, our study neglects technical details and analyzes the antagonistic dynamics between the rumormonger and the rumor suppressor, which provides a deep understanding of the overall development trend of rumor propagation. To reveal the potentials of the rumormonger and the rumor suppressor, the competence-oriented analysis is proposed, where the sufficient and necessary conditions for the existence of the Nash equilibrium in this rumor game are proved rigorously, helping us to derive steady ratios of people who trust or deny a rumor. To figure out the optimal strategy to strike back the rumormonger, the target-oriented analysis is conducted, in which the analytical solutions when the strategies of both players are static are solved and an iteration algorithm is employed to obtain the numerical solutions when their strategies are dynamic. Both numerical and real-world-data based simulations are adopted to verify the proposed competence-oriented and target-oriented analyses.

The great market success of Blockchain makes it an extremely valuable target for attackers. A well-known attack in Blockchain is the forking attack, where divergent blockchains are produced for inserting some new features to facilitate security breaches. The state-of-the-art works mostly focus on how to detect attacks in real-time transactions, which is in hindsight and cannot deter the forking attack from the root. To take precautions, we employ the large deviation theory to study the vulnerability of blockchain networks incurred by intentional forks from a micro point of view, boosting forward-looking and strategic planning mechanisms for resisting the forking attack. Our study is fine-grained, because it offers not only the vulnerability probability of a blockchain network but also its decay speed, through which we find setting the parameter related to the robust level has more power than enhancing the computer power in speeding up the failure of attacks. This finding is valuable since it renders an opportunity to improve the robustness of a blockchain network in a cost-efficient way. Our analysis is complementary, since it studies both the impacts of the computational power as well as the number of confirmations on the vulnerability of a blockchain network, providing a theoretical basis to design reasonable schemes for invigorating a blockchain network from technical as well as managerial levels. Extensive experiments carried out on a large-scale cloud platform running the Ethereum protocol show the experimental and analytical results match well, verifying the effectiveness of our analysis.

In Bitcoin's incentive system that supports open mining pools, block withholding attacks incur huge security threats. In this paper, we investigate the mutual attacks among pools as this determines the macroscopic utility of the whole distributed system. Existing studies on pools' interactive attacks usually employ the conventional game theory, where the strategies of the players are considered pure and equal, neglecting the existence of powerful strategies and the corresponding favorable game results. In this study, we take advantage of the Zero-Determinant (ZD) strategy to analyze the block withholding attack between any two pools, where the ZD adopter has the unilateral control on the expected payoffs of its opponent and itself. In this case, we are faced with the following questions: who can adopt the ZD strategy? individually or simultaneously? what can the ZD player achieve? In order to answer these questions, we derive the conditions under which two pools can individually or simultaneously employ the ZD strategy and demonstrate the effectiveness. To the best of our knowledge, we are the first to use the ZD strategy to analyze the block withholding attack among pools.

Internet of Things (IoT)-based E-commerce is a new business model that relies on autonomous transaction management on IoT-devices. The management system toward IoT-based E-commerce demands autonomy, lightweight, and legitimacy. As blockchain is an innovative technology that is competent in governing the decentralized network, we adopt it to design the autonomous transaction management system on IoT E-commerce. However, current blockchain solutions, most namely cryptocurrencies, have fatal drawbacks of nonsupervisability and huge computational overhead, and hence cannot be directly applied on IoT-based E-commerce. In this paper, we propose NormaChain, a blockchain-based normalized autonomous transaction settlement system for IoT-based E-commerce. By designing a special three-layer sharding blockchain network, we can significantly increase transaction efficiency and system scalability. Additionally, by designing an innovative decentralized public key searchable encryption scheme (decentralized public key encryption with keyword search (PEKS) scheme), we can uncover illegal and criminal transactions and achieve crime traceability. Our new decentralized PEKS scheme cryptographically eliminates the dependence of a trusted central authority in the original PEKS scheme and instead expands it to a fully decentralized governance, which distributes the supervision power equally among all parties. More importantly, by proving NormaChain is secure against chosen ciphertext attacks and against the stealing of the secret key, we show that NormaChain prevents a legitimate user’s privacy from being violated by banks, supervisors or malicious adversaries. Finally, we deliver the NormaChain system with design details and full implementations. Experiments show that the average transaction-per-second on IoT devices is around 113, and the supervision accuracy is 100% when proper target illegal keywords are provided.

Internet of Things (IoT), coming with billions of connected devices, could potentially transform our daily life but could also create a serious security headache. It brings greater complications in securely accessing these devices with privacy protection guaranteed, and several research issues need to be investigated in detail, e.g., access control, traceability, anonymity, authentication, security bootstrap, etc. Most of the traditional security protection mechanisms are centralized, which make them difficult to scale up to meet the security demands of the IoT.

BACKGROUND: Global maritime trade plays an important role in the modern transportation industry. It brings significant economic profit along with bioinvasion risk. Species translocate and establish in a non-native area through ballast water and biofouling. Aiming at aquatic bioinvasion issue, people proposed various suggestions for bioinvasion management. Nonetheless, these suggestions only focus on the chance of a port been affected but ignore the port's ability to further spread the invaded species. RESULTS: To tackle the issues of the existing work, we propose a biosecurity triggering mechanism, where the bioinvasion risk of a port is estimated according to both the invaded risk of a port and its power of being a stepping-stone. To compute the invaded risk, we utilize the automatic identification system data, the ballast water data and marine environmental data. According to the invaded risk of ports, we construct a species invasion network (SIN). The incoming bioinvasion risk is derived from invaded risk data while the invasion risk spreading capability of each port is evaluated by s-core decomposition of SIN. CONCLUSIONS: We illustrate 100 ports in the world that have the highest bioinvasion risk when the invaded risk and stepping-stone bioinvasion risk are equally treated. There are two bioinvasion risk intensive regions, namely the Western Europe (including the Western European margin and the Mediterranean) and the Asia-Pacific, which are just the region with a high growth rate of non-indigenous species and the area that has been identified as a source for many of non-indigenous species discovered elsewhere (especially the Asian clam, which is assumed to be the most invasive species worldwide).

Friend-recommendation applications as one kind of typical social applications can satisfy the social contact needs of different users and become tools for developing a social relationship. However, the privacy leakage has turned into an insurmountable obstacle to the market success of such applications. Existing privacy protection approaches for social applications either introduce untrusted third parties or sacrifice information accuracy. As for friend-recommendation applications particularly, the multihop trust chain and anonymous message methods still have a defect that the hacker can act as a user to acquire information. In this paper, we put forward the privacy protection mechanism based on zero knowledge without any privacy leakage to the application server. In detail, the server knows nothing about the user’s information, but can still provide users with accurate information on friend recommendation. We also analyze the potential attack methods and propose the corresponding solution. Our simulation results verify the effectivity and efficiency of our scheme.

Due to the complexity and volume, outsourcing ciphertexts to a cloud is deemed to be one of the most effective approaches for big data storage and access. Nevertheless, verifying the access legitimacy of a user and securely updating a ciphertext in the cloud based on a new access policy designated by the data owner are two critical challenges to make cloud-based big data storage practical and effective. Traditional approaches either completely ignore the issue of access policy update or delegate the update to a third party authority; but in practice, access policy update is important for enhancing security and dealing with the dynamism caused by user join and leave activities. In this paper, we propose a secure and verifiable access control scheme based on the NTRU cryptosystem for big data storage in clouds. We first propose a new NTRU decryption algorithm to overcome the decryption failures of the original NTRU, and then detail our scheme and analyze its correctness, security strengths, and computational efficiency. Our scheme allows the cloud server to efficiently update the ciphertext when a new access policy is specified by the data owner, who is also able to validate the update to counter against cheating behaviors of the cloud. It also enables (i) the data owner and eligible users to effectively verify the legitimacy of a user for accessing the data, and (ii) a user to validate the information provided by other users for correct plaintext recovery. Rigorous analysis indicates that our scheme can prevent eligible users from cheating and resist various attacks such as the collusion attack.

Crowdsourcing is a promising paradigm to accomplish a complex task via eliciting services from a large group of contributors. However, recent observations indicate that the success of crowdsourcing is being threatened by the malicious behaviors of the contributors. In this paper, we analyze the malicious attack problem using an iterated prisoner's dilemma (IPD) game and propose a zero-determinant (ZD) strategy based scheme by rewarding a worker's cooperation or penalizing the defection for enticing his final cooperation. Both theoretical analysis and simulation study indicate that the proposed algorithm has two attractive characteristics: 1) the requestor can incentivize the worker to keep on cooperating by only increasing the short-term payment; and 2) the proposed algorithm is fair, so the requestor cannot arbitrarily penalize an innocent worker to increase her payoff even though she can dominate the game. To the best of our knowledge, we are the first to use the ZD strategy to stimulate both players to cooperate in an IPD game. Moreover, our proposed algorithm is not restricted to solve the problem of the malicious crowdsourcing - it can be employed to tackle any problem that can be formulated by an IPD game.

The peer-to-peer transmission is a mainstream in challenged network environments. Yet, the free rider phenomenon in peer-to peer transmissions presses a need for incentive mechanisms to stimulate contributions of data transmission. As a result, it is imperative to answer the questions: whether and to what extent an incentive mechanism can invoke such contributions? To answer these questions, we employ an n-player continuous quantum game model to analyze extrinsic incentive mechanisms (promoting cooperative behaviors by offering rewards), and use the quantum prisoner's dilemma model to analyze intrinsic incentive mechanisms (encouraging reciprocal cooperation by exploiting internal bounds). To the best of our knowledge, we are the first to analyze incentive mechanisms for peer-to-peer transmissions from a quantum game perspective. Such a perspective is adopted because the extended strategy space in the quantum game broadens the range for searching optimal strategies and the introduction of entanglement makes the proposed analytical frameworks more practical due to the consideration of the peers' relationships in decision-making. Our proposed quantum game-based analytical frameworks are generic because they are compatible with classic game-based schemes. Our analytical results can provide straightforward insights on evaluating the potential of incentive mechanisms and can serve as important references for designing new incentive mechanisms.

Crowdsourcing applications are vulnerable to malicious behaviors, posing serious threats to their adoption and large deployment. Based on the notion that the requestor (i.e., the crowdsourcer) can block malicious behaviors via leveraging the market power through task allocation and pricing, we propose two novel frameworks based on the mechanism design game theory (i.e., the reverse game theory). To the best of our knowledge, we are the first to exploit the market power and to apply the mechanism design game theory in thwarting malicious behaviors in crowdsourcing. The first proposed framework is built on a requestor-dominant mechanism design game (Rd-MDG), where the game rule is determined solely by the requestor. The second proposed framework is based on the worker-assisted mechanism design game (WaMDG), where the worker (i.e., the contributor) can assist the requestor to determine the game rules by offering advices. These two frameworks have the following salient features: i) neither of them requires the workers to reveal their private information; ii) the game rules of each framework are designed to be able to force the workers to calculate their best strategies based on their actual private information; iii) our theoretical analysis shows that equilibriums exist for both frameworks; and iv) our extensive simulation results demonstrate that these two frameworks can thwart malicious behaviors by driving the workers with a higher attack intent into obtaining lower utilities.

As a virtual backbone structure, connected dominating sets (CDSs) play an important role in topology control for wireless networks. In this paper, we develop a distributed self-stabilizing CDS construction algorithm under the SINR model (also known as the physical interference model), a more practical yet more challenging interference model for distributed algorithm design. Specifically, we propose a randomized distributed algorithm that can construct a CDS in O (log n) timeslots with a high probability, where n is the total number of nodes in the network. The constructed CDS achieves constant approximation in both density and diameter. To the best of our knowledge, this is the first known asymptotically optimal self-stabilizing result in terms of both density and diameter for distributed CDS construction under the practical SINR model.

Personalized applications often provide their functionality by extracting sensitive data from users. Such a strategy brings potential threats to users' privacy because malicious applications may sell users' sensitive data to third-parties for economic interests. The state-of-the-art literature addresses the privacy issue mainly from a technical perspective. In this paper, we take a different angle in which the main players involving privacy leakage are studied from a connected perspective rather than an isolated one. More specifically, we propose the concept of application ecosystem, which consists of user, application, and adversary (malicious third-party) as entities. Our aim is to analyze the tension forces inside the application ecosystem and their impacts on the behavior of each player, which can serve as a theoretical basis for designing effective and efficient privacy preservation solutions from a management level. Another outstanding trait of our analysis is the adoption of quantum game theory to model the application ecosystem, which is suitable because the important property of entanglement in quantum games can be employed to well depict the inner tension forces among the user, application, and adversary. This makes us take an important step towards understanding the complexity of decision-making from rational individuals. To the best of our knowledge, we are the first to quantize the privacy leakage issue. The simulation results quantitatively demonstrate how the mutual restrictions among all players determine their strategies and hence the development of the application ecosystem.

The use of mobile phones while driving has become a major source of distraction to drivers, leading to a large number of car accidents. In this paper, we study the problem of automatically detecting driver phone calls by monitoring smartphone activities and utilizing the vehicle on-board unit. The challenges to overcome include: i) passenger phone calls should be allowed while the calls of the driver should be blocked; ii) the detection mechanism should be phone position-independent and phone owner-independent as the driver may put the smartphone at any position in the front row and make calls via an earphone, or the driver may borrow a passenger's phone to make a call; iii) the in-vehicle environment is noisy resulted from the operating engine, the music the driver and passenger may listen to, and the conversation between passengers and/or the driver; and iv) the computational cost at the smartphone should be light as realtime phone call detection is expected to effectively block an ongoing call to and from the driver. To overcome these challenges and achieve our objective of detecting driver phone calls, we take advantage of the uniqueness of individual's voice features. Through a short period of learning stage, our proposed system can recognize the driver's voice from the collected audio data. Combined with the smartphone's call state, our scheme can determine whether the driver is participating in the current phone call or not. Our strategy takes into account the complicated in-vehicle environment, and the proposed algorithm does not rely on the location of the phone within the vehicle nor the ownership of the smartphone, as the most existing driver phone call detection mechanisms do. We develop a client-server based system with the smartphones being the clients and the vehicle on-board unit being the server. To validate our mechanism, we perform extensive real-world experiments under different scenarios. The results demonstrate a high probability of detecting driver phone calls with a small false alarm rate.

As a promising technology, crowdsourcing aims to accomplish a complex task via eliciting services from a large group of workers. However, recent observations indicate that the success of crowdsourcing is being hindered by the malicious behaviors of the workers. In this paper, we analyze the attack problem using an iterated prisoner's dilemma (IPD) game and propose an zero-determinant (ZD) strategy based algorithm. Simulation results demonstrate that the requestor can incentivize the worker to keep on cooperating.

Cognitive radio provides an efficient technology to solve the problem of spectrum resource scarcity while cooperative communications can increase channel capacity. It is a common sense that combining the two benefits the system performance of cognitive radio networks (CRNs). A challenging problem of intelligent cooperation in CRNs is how to make control decisions when a secondary user cooperates with a primary user to get an optimal cooperation outcome. In this paper, we consider a scenario where a secondary user provides effort to the primary user to win the competition from many secondary users and simultaneously achieves optimal throughput. We establish a novel cooperation scheme termed Low Price to Win (LPW), and abstract the cooperation problem in CRNs as an optimization problem with multiple constraints. Unlike some traditional methods that provide direct solutions, we design a novel greedy algorithm using the Lyapunov optimization technique, by which the sophisticated optimization problem can be divided into a few subproblems and then cross-layer optimization can be applied. Our simulation results demonstrate the efficiency and effectiveness of the proposed algorithm.

As a distributed system, a wireless network, usually faces a complex environment (transient faults and topology changes occur frequently). The connected dominating set (CDS) problem has been widely studied due to its important applications in wireless communication and networks, especially the important role as a virtual backbone for efficient routing. In this paper, under SINR (Signal-to-Interference-plus-Noise-Ratio) model, we propose a distributed self-stabilizing maximal independent set (MIS) algorithm (DSSMIS). Based on DSSMIS, we design a distributed self-stabilizing algorithm (DSSCDS) for CDS construction with constant approximation within O(log n) rounds. To best of our knowledge, this is the first self-stabilizing CDS algorithm under SINR model.

Cooperative content offloading is a promising technology to lessen heavy burden of wireless networks and improve the quality of downloading services. Since few users are voluntary in providing free assistance, auction-based incentive mechanisms are designed to encourage participation. In existing auction-based incentive mechanisms, each provider only acts as a service seller. However, a provider could also be a partner of the requestor if having interest in the requested content. This dual identity of the provider can improve the quality of its service and cut down the payment of requestor. Based on this observation, we propose an auction-based incentive mechanism named CADRE. To the best of our knowledge, CADRE is the first auction-based incentive mechanism that considers the provider's dual identity in cooperative content offloading applications. We prove that CADRE possesses attractive characteristics, i.e., truthfulness, lightweight and privacy protection. Besides, we also demonstrate that CADRE outperforms the traditional multi-attribute second-score sealed reverse auction. Our simulation results verify the theoretical analysis.

Mobile opportunistic networks make use of a new networking paradigm that takes advantage of node mobility to distribute information. Studying their inherent properties of information dissemination can provide a straightforward explanation on the potentials of mobile opportunistic networks to support emerging applications such as mobile commerce, emergency services, and so on. In this paper, we investigate the inherent properties of information dissemination using the Lévy mobility model to characterize the movement pattern of the nodes. Because Lévy mobility can closely mimic human walk, the analysis model we adopt is practical. Our analyses are taken from the perspectives of small- and large-scales. From the perspective of small-scale, the distribution of the minimum time needed by the information to spread to a given region is investigated, from the perspective of large-scale, the bounds of the probability of the earliest time at which the information arrives in a region that is sufficiently farther away are obtained. We also provide the rate that such probability approaches zero as the distance to the region increases to infinity. Finally, our main results are validated by the numerical simulations.

In this paper, we investigate the problem of achieving proportional fairness via access point (AP) association in multirate WLANs. This problem is formulated as a nonlinear programming with an objective function of maximizing the total user bandwidth utilities in the whole network. Such a formulation jointly considers fairness and AP selection. We first propose a centralized algorithm Non-Linear Approximation Optimization for Proportional Fairness (NLAO-PF) to derive the user-AP association via relaxation. Since the relaxation may cause a large integrality gap, a compensation function is introduced to ensure that our algorithm can achieve at least half of the optimal in the worst case. This algorithm is assumed to be adopted periodically for resource management. To handle the case of dynamic user membership, we propose a distributed heuristic Best Performance First (BPF) based on a novel performance revenue function, which provides an AP selection criterion for newcomers. When an existing user leaves the network, the transmission times of other users associated with the same AP can be redistributed easily based on NLAO-PF. Extensive simulation study has been performed to validate our design and to compare the performance of our algorithms to those of the state of the art.

In mobile ad-hoc cognitive radio networks (CRNs), end-to-end paths with available spectrum bands for secondary users may exist temporarily, or may never exist, due to the dynamism of the primary user activities. Traditional CRN routing algorithms, which typically ignore the intermittent connectivity of network topology, and traditional mobility-assisted routing algorithms, which generally overlook the spectrum availability, are obviously unsuitable. To tackle this challenge, we propose a Mobility-Assisted Routing algorithm with Spectrum Awareness (MARSA) to select relays based on not only the probability that a node meets the destination but also the chance at which there exists at least one available channel when they meet. To the best of our knowledge, this paper is the first to bring the idea of mobility-assisted routing to deal with the intermittently connected attribute of mobile ad-hoc CRNs, and the first to enhance the mobility-assisted routing by considering the temporal , spatial, and spectrum domains at the same time. Our simulation results demonstrate the superiority of MARSA over traditional algorithms in intermittently connected mobile CRNs.

Mobile P2P networking is an enabling technology for mobile devices to self-organize in an unstructured style and communicate in a peer-to-peer fashion. Due to user mobility and/or the unrestricted switching on/off of the mobile devices, links are intermittently connected and end-to-end paths may not exist, causing routing a very challenging problem. Moreover, the limited wireless spectrum and device resources together with the rapidly growing number of portable devices and amount of transmitted data make routing even harder. To tackle these challenges, the routing algorithms must be scalable, distributed, and light-weighted. Nevertheless, existing approaches usually cannot simultaneously satisfy all these three requirements. In this paper, we propose two opportunistic routing algorithms for intermittently connected mobile P2P networks, which exploit the spatial locality, spatial regularity, and activity heterogeneity of human mobility to select relays. The first algorithm employs a depth-search approach to diffuse the data towards the destination. The second one adopts a depth-width-search approach in a sense that it diffuses the data not only towards the destination but also to other directions determined by the actively moving nodes (activists) to find better relays. We perform both theoretical analysis as well as a comparison based simulation study. Our results obtained from both the synthetic data and the real world traces reveal that the proposed algorithms outperform the state-of-the-art in terms of delivery latency and delivery ratio.

Abstract Traditional mobile multicast schemes have higher multicast tree reconfiguration cost or multicast packet delivery cost. Two costs are very critical because the former affects the service disruption time during handoff while the latter affects the packet delivery delay. Although the range‐based mobile multicast (RBMoM) scheme and its similar schemes offer the trade‐off between two costs to some extent, most of them do not determine the size of service region, which is critical to the network performance. Hence, we propose a dynamic region‐based mobile multicast (DRBMoM) to dynamically determine the optimal service region for reducing the multicast tree reconfiguration and multicast packet delivery costs. DRBMoM provides two versions: (i) the per‐user version, named DRBMoM‐U, and (ii) the aggregate‐users version, named DRBMoM‐A. Two versions have different applicability, which are the complementary technologies for pursuing efficient mobile multicast. Though having different data information and operations, two versions have the same method for finding the optimal service region. To that aim, DRBMoM models the users' mobility with arbitrary movement directional probabilities in 2‐D mesh network using Markov Chain, and predicts the behaviors of foreign agents' (FAs') joining in a multicast group. DRBMoM derives a cost function to formulate the average multicast tree reconfiguration cost and the average multicast packet delivery cost, which is a function of service region. DRBMoM finds the optimal service region that can minimize the cost function. The simulation tests some key parameters of DRBMoM. In addition, the simulation and numerical analyses show the cost in DRBMoM is about 22∼50% of that in RBMoM. At last, the applicability and computational complexity of DRBMoM and its similar scheme are analyzed. Copyright © 2010 John Wiley & Sons, Ltd.

Mobile IPv6 (MIPv6) offers a basic solution to support mobility in IPv6 networks. Although Hierarchical MIPv6 (HMIPv6) has been designed to enhance the performance of MIPv6 by hierarchizing the network, it does not always outperform MIPv6. In fact, two solutions have different application scopes. Existing work studies the impact of various parameters on the performance of MIPv6 and HMIPv6, but without analyzing their application scopes. In this paper, we propose a model to analyze the application scopes of MIPv6 and HMIPv6, through which an Optimal Choice of Mobility Management (OCMM) scheme is designed. Different from the existing work that either propose new mobility management schemes or enhance existing mobility management schemes, OCMM chooses the better alternative between MIPv6 and HMIPv6 according to the mobility and service characteristics of users, addressing whether to hierarchize the network. Besides that, OCMM chooses the best mobility anchor point and regional size when HMIPv6 is adopted, addressing how to hierarchize the network. Simulation results demonstrate the impact of key parameters on the application scopes of MIPv6 and HMIPv6 as well as the optimal regional size of HMIPv6. Finally, we show that OCMM outperforms MIPv6 and HMIPv6 in terms of total cost including average registration and packet delivery costs.

IPv6 transition problem has become one of the key factors which are holding up the development of the next generation Internet. Aiming to solve IPv6 transition problem, several translation and tunneling techniques have been proposed, satisfying the demand of IPv4-IPv6 interconnection and traversing respectively. However, translation techniques can't convert the semantic between IPv4 and IPv6 protocol perfectly, and they have serious limitations in operation complexity and scalability. Researchers tried to decompose, simplify these problems and improve translation techniques accordingly, but they've come to little achievement since these problems result from the very nature of translation. We propose a novel approach of choosing appropriate translation spot to solve these problems in a different angle, and hence make effective use of translation technique. Then we propose a framework for IPv4-IPv6 coexistence called PET, which integrates tunneling and translation to support both traversing and IPv4-IPv6 interconnection, and uses them properly to constitute communication models in different scenarios. Moreover, we put forward PET signaling method to achieve automatic translation spot election and translation context advertisement, as a complement to the framework.

Hierarchical MIPv6 (HMIPv6) is proposed to improve the system performance of Mobile IPv6 (MIPv6). However, HMIPv6 cannot outperform MIPv6 in all scenarios because of its double-registration when a user roams across regions and the longer packet delivery latency. Therefore, to select a proper mobility management scheme between MIPv6 and HMIPv6 becomes an interesting issue, for its potentials in enhancing the capacity and scalability of the system. In this paper, we develop an analytical model to analyze the applicability of MIPv6 and HMIPv6. Based on this model, we design an Intelligent Mobility Support (IMS) scheme that selects the better alternative between MIPv6 and HMIPv6 for a user according to its changing mobility and service characteristics. When HMIPv6 is adopted, IMS chooses the best mobility anchor point and regional size to optimize the system performance. Numerical results illustrate the impact of some key parameters on the applicability of MIPv6 and HMIPv6. Finally, it is demonstrated that IMS outperforms MIPv6 and HMIPv6.

An adaptive threshold-based call admission control (CAC) scheme used in wireless/mobile network for multi-class services is proposed. In the scheme, each class' CAC thresholds are solved through establishing a reward-penalty model which tries to maximize network's revenue in terms of each class's average new call arrival rate and average handoff call arrival rate, the reward or penalty when network accepts or rejects one class's call etc. To guarantee the real time running of CAC algorithm, an enhanced genetic algorithm is designed. Analyses show that the CAC thresholds indeed change adaptively with the average call arrival rate. The performance comparison between the proposed scheme and mobile IP reservation (MIR) scheme shows that with the increase of average call arrival rate, the average new call blocking probability (CBP) and the average handoff dropping probability (HDP) within 2000 simulation intervals of the proposed scheme are confined to lower levels, and they show approximatively periodical trends of first rise and then decline. While these two performance metrics of MIR always increase. At last, the analysis shows the proposed scheme outperforms MIR in terms of network's revenue