In prior research, spatial coverage has been the primary metric for assessing the quality of mobile network data, with various participant selection strategies developed for data brokers operating within budget constraints. This paper introduces a novel data participant service quality model that prioritizes participant privacy while ensuring creditworthiness and participation willingness. This paper proposes a selection strategy for mobile network data participants that operates under the principles of local differential privacy, designed to maximize the marginal gain under a cost budget constraint. Drawing upon the principle of marginal gains from economics, our strategy advocates for the selection of participants whose marginal gains exceed their associated costs, thereby optimizing the selection process based on relative marginal costs. This paper formalizes this approach as the marginal problem of data participant selection in mobile network data markets, constructing a model and providing theoretical analysis to establish its NP-hard. To address this problem, we develop the Random Response Adaptive Algorithm (RRAA), designed to select participant groups that maximize mobile network data gains. Extensive experiments on both real and simulated datasets demonstrate the algorithm's efficiency and effectiveness. Additionally, to safeguard participant privacy, we propose the Random Response Self-Adaptive Algorithm (RRSAA). Our experimental evaluation on real datasets not only validates the algorithm's privacy-preserving capabilities but also elucidates the impact of privacy preservation parameters on marginal gains.

Traffic flow prediction is crucial for efficient urban planning, traffic management, and user navigation. Modern deep learning models have achieved great success in capturing the complex spatio-temporal dependencies in traffic networks. However, due to frequently changing traffic patterns, the performance of deployed models degrades over time, necessitating periodic updates. Full-scale model retraining is computationally expensive, creating a critical conflict between maintaining prediction accuracy and minimizing update overhead. To address this, incremental update or continual learning methods have emerged, but existing approaches are often tightly coupled with specific model architectures and rely on unclear criteria for data selection, thereby causing redundant data selection and lacking interpretability. To overcome these limitations, we propose a lightweight continual learning method based on B-splines. This method identifies the most valuable data for model updates by analyzing the intrinsic geometric and statistical properties of the traffic data itself. Specifically, we fit a B-spline curve to create a smooth representation of the core traffic pattern and then compute the regression leverage score for each data point to quantify its structural importance. This strategy decouples the data evaluation process from the internal mechanisms of the prediction model. Since the selected data points directly reflect key features of the traffic pattern-such as peaks, inflection points, and anomalies-our method is inherently interpretable, allowing users to understand why certain data points are chosen. Extensive experiments on multiple real-world datasets demonstrate that our method maintains a high level of prediction accuracy while significantly reducing the computational cost of model updates, offering an efficient and transparent solution for the maintenance of dynamic traffic systems.

Spatial crowdsourcing has become popular in recent years, but traditional tasks focus on one-to-one services with single skills like food delivery and ride-hailing. As societal needs grow more complex, there is a need for tasks requiring teams with multiple skills. Current team formation methods using workers from a single platform limit skill diversity, leading to potential task delays, lower quality, and revenue losses. Although cross-platform cooperation offers a potential solution to skill diversity limitations, it faces two challenges: (1) Data protection regulations mandate that platform's raw data must remain localized; (2) cross-platform cooperation incurs additional cooperation costs. To address these challenges, we first define the Cross-platform Online Team Formation (COTF) problem. We then propose a COTF framework and Random Cooperation Strategy to solve COTF problem. To enhance the effectiveness of cooperation, we further propose Precision Query Range Optimization Strategy (PQROS) for worker selection through adaptive range queries, and Dynamic Query Optimization (DQO) for cost-effective scheduling via predictive revenue modeling. Extensive experiments on real and synthetic datasets validate the effectiveness of our proposed methods.

The generation and defense of text adversarial samples are crucial for improving the robustness and security of social Internet of Things (SIoT) systems, as the exchange of information between devices in SIoT relies heavily on NLP technology. However, the discrete nature of text data leads to a lack of contextual integration in current mainstream adversarial sample generation methods based on text replacement. This results in poor stealth of the generated samples and inefficiencies due to excessive queries to the target model. Meanwhile, defense methods like adversarial training are insufficiently universal and generalizable to handle the diverse and complex range of adversarial attack strategies. This article proposes a contrastive learning-based method for generating text adversarial samples and a mutual information regularization-based method for defending against text adversarial samples, tailored to the characteristics of devices in SIoT systems and the challenges mentioned above. The proposed method leverage keyword localization, optimal perturbation, and candidate set evaluation to enhance the effectiveness of adversarial samples. Additionally, by combining mutual information measures, statistical estimation functions, and idempotent constraints, the model itself is equipped with effective defenses against adversarial samples. Compared to the baseline, the proposed method significantly reduce the magnitude of perturbations and the number of access attempts to the original samples, while greatly increasing the attack success rate. When applying the proposed adversarial sample defense method, the model’s accuracy showed a significant improvement after being subjected to adversarial attacks.

The Social Internet of Things (SIoT) system enables connectivity among smart devices by integrating social networks with the Internet of Things. This integration is essential for advancing intelligent services and applications, as well as enhancing the commercial value of data. Rational task and data deployment strategies allow different types of devices to perform optimally in their areas of expertise, reducing network load, improving the timeliness of data processing, and ensuring efficient collaboration across the entire system. However, the limited computational capacity and network bandwidth of SIoT devices result in communication delays that significantly impact job responsiveness and energy consumption. The heterogeneity in computational power and bandwidth across different devices leads to resource overload or underutilization with traditional data partitioning methods, further impacting the performance of SIoT systems. This article proposes an intelligent task and data deployment method to address these issues. The proposed method abstracts the job execution process as JobGraph instance model and optimizes the mapping relationship of operators within TaskProcess. Additionally, an improved linear programming model is used to optimize the data distribution ratio among operators in heterogeneous computing environments. The proposed task deployment method achieves an average improvement of 19.6%–30.2% in job efficiency while reducing interdevice data transmission by 34.2%. In heterogeneous computing environments, the combination of the two deployment optimization methods further reduces job execution time, achieving efficiency improvements of over two times in optimal scenarios.

With the continuous development of spatial crowdsourcing platform, online task assignment problem has been widely studied as a typical problem in spatial crowdsourcing. Most of the existing studies are based on a single-platform task assignment to maximize the platform's revenue. Recently, cross online task assignment has been proposed, aiming at increasing the mutual benefit through cooperations. However, existing methods fail to consider the data privacy protection in the process of cooperation and cause the leakage of sensitive data such as the location of a request and the historical data of cooperative platforms. In this paper, we propose Privacy-preserving Cooperative Online Matching (PCOM), which protects the privacy of the users and workers on their respective platforms. We design a PCOM framework and provide theoretical proof that the framework satisfies the differential privacy property. We then propose two PCOM algorithms based on two different privacy-preserving strategies. Extensive experiments on real and synthetic datasets confirm the effectiveness and efficiency of our algorithms.

Graph keyword search has drawn many research interests, since graph models can generally represent both structured and unstructured databases and keyword searches can extract valuable information for users without the knowledge of the underlying schema and query language. In practice, data graphs can be extremely large, e.g., a Web-scale graph containing billions of vertices. The state-of-the-art approaches employ centralized algorithms to process graph keyword searches, and thus they are infeasible for such large graphs, due to the limited computational power and storage space of a centralized server. To address this problem, we investigate keyword search for Web-scale graphs deployed in a distributed environment. We first give a naive search algorithm to answer the query efficiently. However, the naive search algorithm uses a flooding search strategy that incurs large time and network overhead. To remedy this shortcoming, we then propose a signature-based search algorithm. Specifically, we design a vertex signature that encodes the shortest-path distance from a vertex to any given keyword in the graph. As a result, we can find query answers by exploring fewer paths, so that the time and communication costs are low. Moreover, we reorganize the graph data in the cluster after its initial random partitioning so that the signature-based techniques are more effective. Finally, our experimental results demonstrate the feasibility of our proposed approach in performing keyword searches over Web-scale graph data.

Although$k$NN search on a road network$G_r$, i.e., finding$k$nearest objects to a query user$q$on$G_r$, has been extensively studied, existing works neglected the fact that the$q$'s social information can play an important role in this$k$NN query. Many real-world applications, such as location-based social networking services, require such a query. In this paper, we study a new problem:$k$NN search on road networks by incorporating social influence (RSkNN). Specifically, the state-of-the-artIndependent Cascade(IC) model in social network is applied to define social influence. One critical challenge of the problem is to speed up the computation of the social influence over large road and social networks. To address this challenge, we propose three efficient index-based search algorithms, i.e., road network-based (RN-based), social network-based (SN-based), and hybrid indexing algorithms. In the RN-based algorithm, we employ a filtering-and-verification framework for tackling the hard problem of computing social influence. In the SN-based algorithm, we embed social cuts into the index, so that we speed up the query. In the hybrid algorithm, we propose an index, summarizing the road and social networks, based on which we can obtain query answers efficiently. Finally, we use real road and social network data to empirically verify the efficiency and efficacy of our solutions.

Among uncertain graph queries, reachability, i.e., the probability that one vertex is reachable from another, is likely the most fundamental one. Although this problem has been studied within the field of network reliability, solutions are implemented on a single computer and can only handle small graphs. However, as the size of graph applications continually increases, the corresponding graph data can no longer fit within a single computer's memory and must therefore be distributed across several machines. Furthermore, the computation of probabilistic reachability queries is #P-complete making it very expensive even on small graphs. In this paper, we develop an efficient distributed strategy, called DistR, to solve the problem of reachability query over large uncertain graphs. Specifically, we perform the task in two steps: distributed graph reduction and distributed consolidation. In the distributed graph reduction step, we find all of the maximal subgraphs of the original graph, whose reachability probabilities can be calculated in polynomial time, compute them and reduce the graph accordingly. After this step, only a small graph remains. In the distributed consolidation step, we transform the problem into a relational join process and provide an approximate answer to the #P-complete reachability query. Extensive experimental studies show that our distributed approach is efficient in terms of both computational and communication costs, and has high accuracy.

Objects, such as a digital image, a text document or a DNA sequence are usually represented in a high dimensional feature space. A fundamental issue in (peer-to-peer) P2P systems is to support an efficient similarity search for high-dimensional data in metric spaces. Prior works suffer from some fundamental limitations, such as being not adaptive to a highly dynamic network, poor search efficiency under skewed data scenarios, large maintenance overhead and etc. In this study, we propose an efficient scheme, Dragon, to support P2P similarity search in metric spaces. Dragon achieves the efficiency through the following designs: 1) Dragon is based on our previous designed P2P network, Phoenix, which has the optimal routing efficiency in dynamic scenarios. 2) We design a locality-preserving naming algorithm and a routing tree for each peer in Phoenix to support range queries. A radius-estimated method is proposed to transform a kNN query to a range query. 3) A load-balancing algorithm is given to support strong query processing under skewed data distributions. Extensive experiments verify the superiority of Dragon over existing works.