Given a project, the team formation problem (TFP) aims to find a team of experts with all the required skills and minimize the team’s communication cost. The TFP has emerged as a prominent research topic due to its substantial influence on team performance and the subsequent impact on organizational success. Previous research mainly concentrates on proposing heuristic or metaheuristic algorithms, yet fails to consider the historical experience of solving previous TFP. In addition, most existing learning-based approaches rely on complex multistage workflows, rather than learning the entire process end-to-end. This article proposes an end-to-end deep reinforcement learning approach that focuses on effectively reusing previous experience to tackle the TFP. First, we apply the Markov decision process to model the team formation process, in which the state is primarily characterized by the currently formed team and the skill requirements of the project. Second, we propose a self-updating deep reinforcement learning (SURL) model, in which the social network information serves as the input for the static encoding module, while the skills’ experts and the skill requirements of the project act as the inputs for the dynamic encoding module. Next, a novel deep reinforcement learning algorithm is designed to train the model by fully leveraging the historical experience of team formation. Finally, comprehensive experiments with real-world datasets demonstrate that our proposed model outperforms several commonly used optimization algorithms in terms of the average communication cost.

In recent years, crowdsourcing online food delivery (COFD) services have been increasingly popular, in which a number of crowdsourced riders are recruited to deliver food orders for those geographically dispersed customers. Due to the dynamics and uncertainty of food orders and riders, it is challenging to design an immediate allocation mechanism to recruit suitable riders and plan paths, with the goal of maximizing the total profit of all recruited riders. To address this challenge, we first formalize a crowdsourcing online food delivery problem with the goal of maximizing the expectation of long-term profit of all riders. Then, an efficient heuristic-based algorithm is proposed in which order-exchange and order-transfer policies are applied. Finally, we conduct extensive experiments on synthetic and real-world datasets to evaluate our proposed algorithm, whose results show that the proposed policies and algorithm are more effective compared to the baselines in terms of total profit, total distance, and average waiting time.

Summary In mobile opportunistic networks, the high mobility of humans and the resource limitation of smart mobile devices pose a great challenge to the design of efficient data dissemination, in which data packets generated from publishers need to be delivered to the subscribers via opportunistic encounters. The current data dissemination schemes generally concentrate on the similarity among nodes while ignoring the similarity between nodes and data packets, which leads to that data packets move back and forth among the nodes with similar social features instead of reaching to the subscribers efficiently. In this paper, an efficient interest‐aware data dissemination approach is proposed in mobile opportunistic networks, in which the similarity between nodes and data packets is used to determine whether a node is a potential destination. Moreover, both the social feature and the residual energy are considered to choose an appropriate relay node, and then determine the number of packet's replicas. The simulation results show that efficient interest‐aware data dissemination provides high efficiency and less transmission overheads compared with the traditional approaches.

In mobile crowdsensing, numerous smartphone users fulfill a complex environmental or social task in a cooperative way, in which participant recruitment or task allocation is a fundamental issue. Various mechanisms have been proposed to motivate normal users to participate in sensing tasks or provide high-quality sensing data. However, there exist some conflicts among tasks and participants, which make participant recruitment a challenging issue. For this issue, a conflict-aware participant recruitment (CAPR) mechanism is proposed for mobile crowdsensing, where there may exist task correlations and conflicts. First, two definitions of conflicts are introduced, and then, the participant recruitment problem is formalized. Next, an efficient heuristic algorithm is proposed followed by the payment determination and reputation update of participants. Simulation results indicate that the proposed mechanism can effectively improve the platform utility and the average task quality while guaranteeing no conflicts in fulfilling sensing tasks.

Differential privacy (DP) provides a rigorous and provable privacy guarantee and assumes adversaries’ arbitrary background knowledge, which makes it distinct from prior work in privacy preserving. However, DP cannot achieve claimed privacy guarantees over datasets with correlated tuples. Aiming to protect whether two individuals have a close relationship in a correlated dataset corresponding to a weighted network, we propose a differentially private network data release method, based on edge correlation, to gain the tradeoff between privacy and utility. Specifically, we first extracted the Edge Profile (PF) of an edge from a graph, which is transformed from a raw correlated dataset. Then, edge correlation is defined based on the PFs of both edges via Jenson-Shannon Divergence (JS-Divergence). Secondly, we transform a raw weighted dataset into an indicated dataset by adopting a weight threshold, to satisfy specific real need and decrease query sensitivity. Furthermore, we propose ϵ -correlated edge differential privacy (CEDP), by combining the correlation analysis and the correlated parameter with traditional DP. Finally, we propose network data release (NDR) algorithm based on the ϵ -CEDP model and discuss its privacy and utility. Extensive experiments over real and synthetic network datasets show the proposed releasing method provides better utilities while maintaining privacy guarantee.

With the proliferation of smartphones and the usage of the smartphone apps, privacy preservation has become an important issue. The existing privacy preservation approaches for smartphones usually have less efficiency due to the absent consideration of the active defense policies and temporal correlations between contexts related to users. In this paper, through modeling the temporal correlations among contexts, we formalize the privacy preservation problem to an optimization problem and prove its correctness and the optimality through theoretical analysis. To further speed up the running time, we transform the original optimization problem to an approximate optimal problem, a linear programming problem. By resolving the linear programming problem, an efficient context-aware privacy preserving algorithm (CAPP) is designed, which adopts active defense policy and decides how to release the current context of a user to maximize the level of quality of service (QoS) of context-aware apps with privacy preservation. The conducted extensive simulations on real dataset demonstrate the improved performance of CAPP over other traditional approaches.

The advances in sensing capabilities of smartphones give rise to a variety of mobile participatory sensing applications that collect users' personal data. Because of the existence of both sensitive, private personal data, and untrusted aggregator, serious privacy concerns on users arise. Currently, existing privacy preserving data collection methods either require bidirectional communications between an untrusted aggregator and mobile users in every aggregation period, or have high computation or communication overhead. To address these problems, we propose an efficient data aggregation approach by which an untrusted aggregator in mobile sensing can collect the statistics over the data contributed by multiple mobile users, while supporting privacy preservation of each user and data integrity verification. In this approach, information hiding and homomorphic encryption are applied to guarantee the data privacy of mobile users. In detail, a breadth-first search tree is first constructed at the initial phase among the mobile users, and then the original datum of each user is perturbed among its neighbors in ciphertext space by using information hiding and homomorphic encryption. The evaluations of our approach show that our protocol requires lower communication and computation overhead and thus more feasible for the computation constrained mobile devices. Copyright © 2016 John Wiley & Sons, Ltd.

Users can enjoy personalized services provided by various context-aware applications that collect users' contexts through sensor-equipped smartphones. Meanwhile, serious privacy concerns arise due to the lack of privacy preservation mechanisms. Currently, most mechanisms apply passive defense policies in which the released contexts from a privacy preservation system are always real, leading to a great probability with which an adversary infers the hidden sensitive contexts about the users. In this paper, we apply a deception policy for privacy preservation and present a novel technique, FakeMask, in which fake contexts may be released to provably preserve users' privacy. The output sequence of contexts by FakeMask can be accessed by the untrusted context-aware applications or be used to answer queries from those applications. Since the output contexts may be different from the original contexts, an adversary has greater difficulty in inferring the real contexts. Therefore, FakeMask limits what adversaries can learn from the output sequence of contexts about the user being in sensitive contexts, even if the adversaries are powerful enough to have the knowledge about the system and the temporal correlations among the contexts. The essence of FakeMask is a privacy checking algorithm which decides whether to release a fake context for the current context of the user. We present a novel privacy checking algorithm and an efficient one to accelerate the privacy checking process. Extensive evaluation experiments on real smartphone context traces of users demonstrate the improved performance of FakeMask over other approaches.