Differential-privacy federated learning (DP-FL) has emerged as a promising paradigm capable of mitigating the inherent threat of traditional FL architectures that are vulnerable to inferential attacks due to the frequent exchange and updating of model parameters. However, existing DP-FL frameworks often assume that the client's perturbations remain constant throughout the FL process, while ignoring the varying influence of the client's perturbations in distinct communication rounds on the model performance. Besides, existing DP-FL frameworks posit the FL server as a fully rational actor, thereby neglecting the bounded rationality that the FL server may exhibit in the face of risk and uncertainty. In this paper, we propose a novel long-term (i.e., throughout the FL process) privacy-preserving FL framework to address the optimal incentive design, in the presence of the bounded rationality inherent in the FL server and the dynamic influence of perturbations on model performance. Specifically, we first investigate the impact of local perturbations of the client on the model's convergence performance in different communication rounds, elucidating the trade-off between learning performance and privacy loss. Then, to reconcile learning performance with privacy loss, we design a long-term privacy-preserving incentive scheme, where the interactions between clients and the FL server throughout the FL process are modeled as a multi-stage privacy-preserving game. Furthermore, by applying prospect theory (PT) to formulate the risk-aware behavior of the bounded rationality FL server, we employ contract theory to derive the equilibrium of the game, thereby ensuring optimality and fairness. Finally, extensive simulations illustrate that our scheme can motivate clients to provide high-quality models and improve the accuracy of the global model, compared with benchmarks.

The convergence of aUtonomous aerial vehicles (UAVs) and federated learning (FL) has emerged as a promising paradigm to facilitate artificial intelligence (AI) services with enhanced privacy preservation. However, notwithstanding the inherent advantages of FL in terms of privacy protection, attackers can still exploit inference attacks to deduce raw data of UAVs. The existing studies predominantly assume FL servers (hereafter servers)to be fully rational and have access to all privacy preference information of UAVs (i.e., information symmetry scenario), in the design of privacy-preserving incentive schemes. To tackle these challenges, we propose a privacy-preserving incentive scheme for UAV-aided FL in the presence of information asymmetry while considering the serverexhibits bounded rationality. Specifically, a practical UAV-aided FL framework is first introduced to enable AI model training between UAVs and the server with bounded rationality. In addition, based on differential privacy, we quantify the privacy level of UAVs and subsequently analyze its impact on the aggregation accuracy of the server. This scenario entails two conflicting objectives: the server aims for higher-quality local models to achieve better aggregation accuracy, while UAVs prioritize injecting more noise into their local models to enhance privacy protection. To reconcile the conflicting objectives, we develop an incentive mechanism based on contract theory to optimize the server’s aggregation accuracy in the presence of information asymmetry. Furthermore, we employ prospect theory (PT) to the above contract to capture biases in the server’s subjective decision-making process. Besides, we deduce closed-form solutions for optimal contracts under PT and expected utility theory (EUT), where participants are assumed to be fully rational. Finally, simulation results validate the superiority of our proposed scheme in motivating UAVs to share high-quality local models and improving the aggregation accuracy of the server.

The integration of unmanned aerial vehicles (UAVs) and artificial intelligence (AI) has garnered significant interest as a promising paradigm for facilitating intelligent and pervasive mobile crowdsensing (MCS) services. In traditional AI methodologies, the centralization of large volumes of privacy-sensitive sensory data shared by UAVs for model training entails substantial privacy risks. Federated learning (FL) emerges as an appealing privacy-preserving paradigm that enables participating UAVs to collaboratively train shared models while safeguarding the privacy of their data. However, given that the execution of FL tasks inherently requires the consumption of resources such as power and bandwidth, rational and self-interested UAVs may not actively engage in FL or launch free-riding attacks (i.e., sharing fake local models) to mitigate costs. To address the above challenges, we propose a truthful incentive scheme in FL-based UAV-assisted MCS. Specifically, we first present a learning framework tailored for realistic scenarios in UAV-assisted MCS that enhances privacy preservation and optimizes communication efficiency during AI model training for collaborative UAVs, where the sensing platform (i.e., the aggregation server) is the finite-rational decision maker. Then, based on prospect theory (PT), we design an incentive mechanism to motivate UAVs to participate in FL. In this mechanism, a PT-based game is exploited to model the interactions between the sensing platform and UAVs, where the equilibrium is derived. Moreover, we employ a zero-payment mechanism to curb the self-interested behavior of UAVs. Finally, simulation results show that the proposed scheme can facilitate high-quality model sharing while suppressing free-riding attacks.

Differential-privacy federated learning (DP-FL) has emerged as a promising approach to mitigate the inherent risks associated with traditional FL architectures, which are susceptible to inferential attacks due to the continuous sharing and updating of model parameters. However, existing DP-FL frameworks typically assume that the perturbations introduced by clients remain constant throughout the FL process, overlooking the dynamic influence of these perturbations on model performance across different communication rounds. In this paper, we present a long-term privacy-preserving FL framework designed to address issues of optimal incentive design, considering the dynamic influence of perturbations on model performance. Specifically, we first analyze the effect of local perturbations on the model’s convergence performance during various communication rounds, elucidating the balance between learning performance and privacy loss. Then, to harmonize learning performance with privacy loss, we develop a long-term privacy-preserving incentive scheme, where the interactions between clients and the FL server throughout the FL process are modeled as a multi-stage privacy-preserving game. Furthermore, we utilize contract theory to derive the equilibrium of this game. Finally, simulations show that our scheme can incentivize clients to contribute high-quality models, thereby enhancing the accuracy of the global model, as compared to benchmarks.

The fusion of federated learning (FL) and unmanned aerial vehicles (UAVs) garnered significant attention as a propitious paradigm, enabling the provision of ubiquitous Artificial Intelligence (AI) services in a privacy-preserving manner. However, despite the intrinsic superiority of FL in safeguarding privacy, an attacker could utilize differential attacks to infer the original data of UAVs. To address the aforementioned challenges, we design a privacy-preserving incentive scheme for UAV-aided FL. In particular, a UAV-aided FL framework is first proposed to facilitate AI model training between UAVs and the server. Then, we quantify the privacy level of UAVs based on differential privacy and analyze its influence on the aggregation accuracy of the server. This scenario involves a complex trade-off between two conflicting objectives. On the one hand, the server desires to obtain higher quality local models for superior aggregation accuracy. On the other hand, UAVs prefer to add more noise to their local models for better privacy protection. Besides, by employing contract theory, we propose an incentive mechanism to optimize the server's aggregation accuracy under information asymmetry. Finally, simulation results validate the superiority and feasibility of our proposed scheme.

Unmanned Aerial Vehicles (UAVs) have attracted extensive attention from both industry and academia owing to their high mobility, line-of-sight (LoS) characteristics of air-toground (A2G) channels, and low cost. However, the broadcast nature of wireless transmission and the LoS characteristics of A2G channels are vulnerable to eavesdropping attack, which leads to severe security issues. To enhance the security of UAV communication, we propose a framework that multiple UAVs cooperate to resist attacks (MURA). Specifically, we first propose an efficient incentive scheme based on the coalitional game to encourage UAVs to join the coalition. We prove that each UAV can maximize its utility by joining the coalition to form a grand coalition. Then, a secure UAV communication scheme is proposed to resist eavesdropping attack. Two types of scenarios are considered for UAV communication. In a completely rational scenario, in which participants make decisions aiming to maximize their utility, we utilize the Stackelberg game to model the interactions between UAVs and attacker. The existence and uniqueness of the equilibrium solution are proved, and the equilibrium solution is obtained. In an imperfectly rational scenario, the prospect theory (PT) is applied to capture the underlying rationality of the players. The PT valuations of the players, i.e., UAV and attacker, are deduced in detail. Meanwhile, the convergence of the PT valuations of UAV and attacker is proved. Finally, extensive simulation results show that the proposed scheme can effectively improve the utility of legal UAVs and ensure the security of the UAV networks compared with benchmarks.

Unmanned aerial vehicles (UAVs) combined with tagging applications (Apps) have recently attracted considerable attention to enable efficient mobile crowdsensing (MCS) applications in scenarios where an insufficient number of UAVs may be available to perform the sensing tasks. However, there remain potential security and incentive threats for App- and UAV-assisted crowdsensing owing to the presence of malicious UAVs and the selfishness of UAVs. To address these issues, we propose a two-stage secure incentive mechanism in the App- and UAV-assisted MCS. Specifically, we first develop an App- and UAV-assisted MCS framework, where the App tags the location of the sensing task as a point-of-interest (PoI) to attract registered UAVs, thus assisting the platform to complete the sensing task efficiently. To motivate the App to cooperate with the sensing platform, we design a double auction-based incentive mechanism for PoI-tagging tasks in the first stage, where the optimal price for PoI-tagging services is obtained by applying a double auction game. Furthermore, we evaluate each UAV through comprehensive consideration of the performance and security of UAVs for most task-suitable UAV recruitment and malicious UAVs prevention. Additionally, in the second stage, based on the Stackelberg game theory, an incentive mechanism for sensing tasks is proposed to encourage UAV participation. Finally, simulation results and security analysis validate that the proposed mechanism can greatly increase the utility of UAVs and the App while ensuring the security of the sensing process.

Application (App)-assisted mobile edge crowd- sensing is a promising paradigm, in which Apps are in charge of tagging the location of the sensing tasks as point-of-interest (PoI) to assist the platform in recruiting users to participate in the sensing tasks. However, there exist potential security, incentive, and privacy threats for App-assisted mobile edge crowdsensing (AMECS) due to the presence of malicious Apps, the low-quality shared sensing data, and the vulnerability of wireless communication. Therefore, we propose a differential privacy-based incentive (DPI) scheme for AMECS to provide secure and efficient crowdsensing services while protecting users’ privacy. Specifically, we first propose an App quality management mechanism to correlate the behavior of each App with its quality and then select reliable Apps based on quality thresholds to assist the platform in recruiting users. With the designed mechanism, we further present an auction game-based incentive mechanism to encourage Apps to mark the location of the sensing tasks as PoI. To protect the privacy of users, a privacy-preserving sensing data sharing algorithm is devised based on differential privacy. Further, given the difficulty of obtaining accurate network parameters in practice, a reinforcement learning-based incentive mechanism is designed to encourage users to participate in sensing tasks. Finally, simulation results and security analysis demonstrate that the proposed scheme can effectively improve the utilities of users, ensure the security of the crowdsensing process, and protect the privacy of users.

The combination of applications (Apps) and mobile edge crowdsensing technology has been viewed as a promising paradigm, where Apps are responsible for marking the location of the sensing task as point-of-interest (PoI) to assist the platform in recruiting users. However, there still exist potential incentive and privacy threats associated with App-assisted mobile edge crowdsensing (AMECS) due to the selfish nature of Apps and the vulnerability of wireless communication. To this end, we propose a differential privacy-based incentive (DPI) scheme for AMECS to support secure and efficient crowdsensing while protecting the privacy of users. Specifically, we first propose an App quality management mechanism to correlate the behavior of the App with its quality and then choose reliable Apps based on quality thresholds. Afterwards, a privacy-preserving sensing data sharing algorithm is designed to protect the privacy of users. Furthermore, given the difficulty of obtaining accurate network parameters in real life, a reinforcement learning-based incentive mechanism is devised to motivate users to actively engage in sensing tasks. Finally, simulation results and security analysis demonstrate that the proposed scheme is effective in improving the utility of participants and protecting the privacy of users.

At present, the crowdsourcing-based indoor navigation system (CINS) has attracted extensive attention from both industry and academia owing to its low-cost and high-accuracy performance. Unfortunately, the system that relies on crowdsourced data is vulnerable to the collusion attack, which leads to severe security issues. To address the security issues in the CINS, we propose to utilize a fully trusted fog server platform to advocate secure transactions between service requesters and responders. First, we propose a novel reputation incentive mechanism based on the behaviors of responders. Then, we employ the offensive and defensive game to model the interactions between the fog server platform and the responders, whereby a social welfare optimization problem is formulated to maximize the social welfare of the system. Next, the game equilibriums are found by using the replicator dynamic equation while the game stability is discussed. Finally, the simulation results show that the proposed mechanism can effectively encourage responders to provide positive navigation services and obtain more social welfare of the system compared with the conventional mechanisms.

Unmanned aerial vehicles (UAVs) combining with crowdsensing technology has been viewed as a promising paradigm for performing sensing tasks in extreme scenarios such as earthquakes, etc. However, potential security issues could incur on data sharing between UAVs and task publishers owing to the vulnerability of central nodes and selfishness of distrusted UAVs. To cope with these problems, we propose a novel blockchain-based crowdsensing framework with reputation incentive (BCFR) in UAV-assisted mobile crowdsensing. Specifically, we first propose a novel reputation incentive scheme to choose UAVs with a high reputation to perform sensing tasks, thereby protecting data sharing between UAVs and task publishers from internal attack (i.e., some UAVs with insufficient resources may turn into malicious UAVs to provide wrong sensory data to the task publishers). Then, we design a blockchain-based secure data transmission scheme to securely record data transactions of UAVs. Furthermore, since UAVs with limited resources are difficult to perform compute-intensive mining tasks, edge computing is incorporated to increase the success probability of block creation. The interactions between UAVs and edge computing provider (ECP) are modeled as a two-stage Stackelberg game to motivate UAVs participating in the block creation process while providing high-quality services. Finally, we conduct extensive simulations to demonstrate that the proposed BCFR scheme can effectively improve successful mining probabilities and utilities of UAVs, and ensure the security of data sharing among UAVs and task publishers.

Unmanned aerial vehicles (UAVs) have attracted extensive attention from both industry and academia owing to their high mobility, and characteristics of line of sight (LoS) propagation. However, wireless communication is vulnerable to eavesdropping attacks because of the broadcast characteristics. To enhance secure UAV communications with the ground nodes, we propose a novel framework that multiple UAVs cooperate to resist attack (MURA). First, we propose an incentive mechanism based on coalitional game to encourage legal UAVs to join the coalition. We prove that each legal UAV can only maximize its profits by joining the coalition to form a major coalition. Then, a secure UAV communication scheme is proposed to resist the eavesdropping attacks. Two types of scenarios are considered for the UAV communication: in a completely rational scenario, we utilize the Stackelberg game to model the interactions between the legal UAVs and attacker. In an imperfectly rational scenario, the cumulative prospect theory (PT) is applied to the game to capture the underlying rationality of the players. Finally, simulation results show that the proposed scheme can significantly improve the security of the UAV network compared with traditional schemes.