Data queries play a crucial role in the vehicular platoon, enabling vehicles to obtain traffic information about surrounding road conditions and personalized entertainment information services. However, data query requests from vehicles may expose the vehicle owner’s personal attributes and habits. Although several schemes can address these issues, they are incapable of countering collusion attacks between the coordinating vehicle and roadside units (RSUs). To solve this problem, in this paper we propose a privacy-preserving and collusion-resistant data query scheme, named PCDQ. Specifically, PCDQ uses the Paillier encryption and Chinese Remainder Theorem to protect the query privacy of vehicle owners, allowing the RSU to recover individual data query requests without associating them with the original vehicles. Next, the parameter update mechanism in PCDQ prevents the coordinating vehicle from obtaining the corresponding mapping information between vehicles and query parameters, thereby resisting collusion attacks between the coordinating vehicle and RSUs. In addition, identity-based signcryption is used to ensure secure parameter distribution among vehicles, and the batch verification enables efficient authentication of query requests. Detailed security proofs and analysis demonstrate that PCDQ satisfies multiple security properties, including resistance to collusion attacks and replay attacks, unlinkability, confidentiality, and authentication and data integrity. Experimental results show that, compared to existing solutions, PCDQ performs better in terms of computation overhead, communication overhead, and network performance.

With the widespread adoption of smartphones and other mobile intelligent devices, Mobile Crowd Sensing (MCS) is widely used. Typically, the real locations of the workers and tasks must be submitted to the service platform to complete the task allocation. Therefore, the protection of location information has become a key factor in influencing user participation. To address the issue of location information leakage, we propose a location information protection method based on local differential privacy, which can protect the location privacy of workers and tasks while generating approximately accurate task allocation results. Firstly, we divide the region into$k$*$k$grids and merge girds with a similar dispersion to form clusters. Then, this paper utilizes the inverse sampling of the cumulative distribution function (CDF) of the flipped Huber distribution to generate a personalized noise location set for each cluster. Furthermore, the exponential mechanism is used to select the obfuscated location for each user. Finally, the platform selects workers based on the perturbed location to complete the task allocation. Theoretical analysis shows that our mechanism satisfies differential privacy and achieves an approximately accurate task allocation. Experimental results demonstrate that, compared to existing methods, this method exhibits superior performance across different datasets and effectively balances the utility of data and the protection of location privacy.

With the rapid advancement of Mobile Crowdsensing (MCS) technology, its impact on daily life continues to grow, making it indispensable to modern society. However, user data collection and analysis pose significant privacy risks. Although existing privacy-preserving task allocation schemes incorporate some basic adjustments for personalized noise, they fail to dynamically adapt based on user distribution and primarily focus on single-objective constraints. To bridge this gap, we propose a Density-Aware Personalized Differential Privacy for Multi-Objective Task Allocation in Mobile Crowdsensing (PDPMTA) scheme that adaptively adjusts noise intensity based on the distribution density of user data in feature space, then obfuscates sensitive information using differential privacy techniques. PDPMTA introduces a hybrid optimization strategy combining Simulated Annealing (SA) with NSGA-II, where simulated annealing is periodically applied to population subsets to balance exploration and exploitation, achieving more effective convergence toward the Pareto front. Experimental results on the real-world datasets confirm the scheme's effectiveness in optimizing travel distance, platform costs, and cost-efficiency.

In modern urban traffic management, accurate traffic flow prediction contributes to travel decision optimization, signal control, emergency response and resource scheduling, which improves road efficiency, reduces congestion and promotes sustainable urban planning. However, traffic flow data are characterized by complex spatio-temporal dependence, multi-scale periodicity, and highly dynamic changes. Existing studies mostly focus on single spatio-temporal domain modeling and ignore frequency domain information fusion, which makes it difficult to comprehensively capture the potential laws of traffic flow. To this end, a multi-scale spatio-temporal fusion Transformer prediction model is proposed, which systematically integrates frequency-domain analysis with spatio-temporal dependent modeling. The model contains three parts: (1) spatial-temporal adaptive neighbor selection algorithm, which dynamically supplements topological information based on spatio-temporal correlation to enhance the efficiency of inter-subdivisional information transfer; (2) frequency-domain feature coupling module, which fuses the frequency-domain and spatial-domain features by fast Fourier transform to enhance the ability of temporal pattern sensing; and (3) spatio-temporal-frequency-domain dual-attention encoder, which combines the linear-attention mechanism to efficiently capture the longrange spatio-temporal dependencies. Experimental results on several real traffic datasets show that the model significantly outperforms existing methods in terms of mean absolute error, root mean square error and mean absolute percentage error, and demonstrates stronger robustness in complex spatio-temporal patterns and abnormal fluctuation scenarios.

Federated learning, owing to its distributed characteristic, is particularly susceptible to data poisoning attacks. To address this issue, a wealth of defenses has been developed that aim to mitigate such attacks by limiting the adverse effects of malicious models on the aggregated result. However, most existing defense methods are designed only from the perspective of data filtering or model weighting, which leads to poor robustness and an exclusive reliance on a single server-side defense mechanism. To mitigate these vulnerabilities, we propose FedD2, a federated defense framework that integrates data augmentation and residual-based weighted aggregation. Firstly, FedD2 applies data augmentation techniques to regenerate and mix local datasets, enhancing data generalization. During the server aggregation stage, FedD2 leverages model residuals to detect abnormal updates and adaptively assign aggregation weights to local models, thereby reducing the influence of malicious clients. Comprehensive experiments performed on benchmark datasets demonstrate that FedD2 significantly enhances classification performance and exhibits strong robustness against data poisoning attacks.

Auctions have been widely applied in resource allocation due to their fairness and efficiency. For instance, platforms receive requests from service requesters and utilize auction theory to select suitable service providers. Existing studies typically assume that winners are determined based on bidders’ true valuations by allowing arbitrary transactions between requesters and providers, which can lead to serious valuation privacy leakage issues and limitations in application scenarios. Although some research has addressed these concerns using differential privacy techniques, they mostly rely on a trusted platform, and the introduction of noise results in utility loss, making them unsuitable for restricted auction contexts. To overcome these limitations, we propose a restricted double auction mechanism based on local differential privacy. Specifically, we extract the characteristics of the valuation data and constrain the noise addition probability density function based on the data features. Then we design a novel exponential selection mechanism that ensures that the relative positions of the obfuscated bids remain unchanged compared to the original valuations, while satisfying ε-local differential privacy. Furthermore, we develop an auction matching mechanism that maintains properties such as truthfulness under restricted allocation. The simulation results demonstrate that the proposed bid obfuscation mechanism ensures that the relative positions of the interfered bids remain unchanged while incurring low time overhead. Compared to existing mechanisms, our restrictive auction mechanism can generate greater social welfare while reducing the risk of valuation privacy leakage.

Cloud service providers typically provide different types of virtual machines (VMs) to cloud users with various requirements. Thanks to its effectiveness and fairness,auctionhas been widely applied in this heterogeneous resource allocation. Recently, several strategy-proof combinatorial cloud auction mechanisms have been proposed. However, they fail to protect the bid privacy of users from being inferred from the auction results. In this article, we design adifferentially privatecombinatorial cloud auction mechanism (DPCA) to address this privacy issue. Technically, we employ the exponential mechanism to compute a clearing unit price vector with a probability proportional to the corresponding revenue. We further improve the mechanism to reduce the running time while maintaining high revenues, by computing a single clearing unit price, or a subgroup of clearing unit prices at a time, resulting in the improved mechanisms DPCA-S and its generalized version DPCA-M, respectively. We theoretically prove that our mechanisms can guarantee differential privacy, approximate truthfulness and high revenue. Extensive experimental results demonstrate that DPCA can generate near-optimal revenues at the price of relatively high time complexity, while the improved mechanisms achieve a tunable trade-off between auction revenue and running time.

Spectrum auction is an effective approach to improve the spectrum utilization, by leasing an idle spectrum from primary users to secondary users. Recently, a few differentially private spectrum auction mechanisms have been proposed, but, as far as we know, none of them addressed the differential privacy in the setting of double spectrum auctions. In this paper, we combine the concept of differential privacy with double spectrum auction design and present a differentially private double spectrum auction mechanism with approximate social welfare maximization (DDSM). Specifically, we design the mechanism by employing the exponential mechanism to select clearing prices for the double spectrum auction with probabilities exponentially proportional to the related social welfare values and then improve the mechanism in several aspects, such as the designs of the auction algorithm, the utility function, and the buyer grouping algorithm. Through theoretical analysis, we prove that DDSM achieves differential privacy, approximate truthfulness, and approximate social welfare maximization. Extensive experimental evaluations show that DDSM achieves a good performance in terms of social welfare.