Crowdsensing has gained widespread attention due to its efficient and low cost data collection mode that leverages a large number of intelligent mobile devices. Privacy and data quality are two key concerns in crowdsensing. Recently, extensive efforts have been devoted to privacy-preserving truth discovery (PPTD), which aims to protect sensitive data while improving data quality. However, existing PPTD schemes suffer from either low reliability—especially under collusion attacks and server dropout—or low communication efficiency. As a result, they fail to meet the practical requirements of real-time processing with high reliability. To address the problems, we propose FPTD, a super fast PPTD scheme for crowdsensing that provides$T$-out-of-$N$threshold reliability, with a focus on boosting online efficiency. Our scheme employs edge nodes as servers in a multi-server architecture, resisting up to ($T-1$) colluding servers and ($N-T$) server dropouts. We construct novel protocols for PPTD, including secure division and negative of approximate logarithms. We further significantly improve communication efficiency by designing protocolsmultiply-then-divide,dot-product-then-divide, andfilter-then-dot-product-then-divide, all of which require only a single element per party in online communication. Our tradeoff is a need for a circuit-dependent offline phase, which is independent of the parties' inputs. Compared to the state-of-the-art scheme, we are 13$\sim 217\times$faster (LAN) and 17$\sim 190 \times$faster (WAN) in online execution time.

Truth discovery is an effective technique for resolving data conflicts in crowdsensing. Fog-based mobile crowdsensing utilizes low-latency and high-efficiency communications capabilities of fog computing to achieve large-scale data sensing at a low cost. Privacy-preserving truth discovery (PPTD) has garnered significant attention in recent years due to the inclusion of users’ sensitive information in sensory data. However, existing PPTDs have not adequately addressed fog servers’ reliability and mobile devices’ efficiency simultaneously. Challenges are that fog servers are susceptible to breakdowns and collusion that causes privacy breaches, while mobile devices have limited resources. We thus propose a reliable and efficient PPTD for fog-based crowdsensing. We employ a threshold secret sharing scheme to establish secure multi-party computation primitives. These primitives are then used to construct an arithmetic circuit–an essential component of the PPTD. This approach preserves privacy of sensory data, as well as intermediate and final results, while accounting for server collusion, dropout, and mobile devices’ efficiency. It has$T$-out-of-$N$threshold reliability that resists ($T-1$) servers’ collusion and ($N-T$) servers’ dropout. Experimental results demonstrate that our scheme reduces worker processing time by at least one order of magnitude and network communication overhead by approximately two orders of magnitude compared to existing PPTD methods.

Most urban logistics providers are able to deliver goods efficiently using conventional vehicles, except in areas that have low quality road infrastructure and steep road slopes, which pose a challenge for delivery. This lowers the quality of delivery services, creating pockets of underserved populations in urban areas, which is undesirable from an economic and social perspective. The flexibility of drones has made them an attractive option for last-mile delivery in this context. Optimizing the utilization of remaining energy and payload capacity during drone return trips is essential for maximizing overall utility. However, prior studies have overlooked the consideration of backhaul in the context of drone-only systems, as well as the assessment of energy consumption under wind conditions. This paper proposes a new model for the Drone Delivery Routing Problem with Backhaul (DDRPB), which incorporates both wind conditions and backhauling requirements, and is applicable for areas that have high delivery demand and return requests, but suffer from low delivery service quality. We introduce a linearized drone-wind-energy consumption model and design a Lagrangian relaxation heuristic algorithm to solve the resulting NP-hard optimization problem efficiently. A case study of an urban area with delivery challenges in Seoul, South Korea is presented. The results show that the proposed model effectively decreases overall costs.

Human intelligence tasks (HITs) are widely utilized for crowdsourcing human knowledge, such as labeling images for machine learning. Centralized crowdsourcing platforms face challenges of a single point of failure and a lack of service transparency. Existing blockchain-based crowdsourcing approaches overlook the low scalability problem of permissionless blockchains or inconveniently rely on existing ground-truth data as the root of trust to evaluate quality of workers' answers. We propose a blockchain-based crowdsourcing scheme for ensuring dual fairness (i.e., preventing false-reporting and free-riding) and improving on-chain efficiency concerning on-chain storage and smart contract computation. The proposed scheme does not rely on trusted authorities but rather depends on a public blockchain to guarantee the dual fairness. An efficient and publicly verifiable truth discovery scheme is designed based on majority voting and cryptographic accumulators. This truth discovery scheme aims at inferring ground truth from workers' answers. The ground truth is further utilized to estimate the quality of workers' answers. Additionally, a novel blockchain-based protocol is designed to further reduce on-chain costs while ensuring truthfulness. The scheme has O(n) complexity for both on-chain storage and smart contract computation, regardless of the number of questions, where n denotes the number of workers. Formal security analysis is provided, and extensive experiments are conducted to evaluate effectiveness and performance.

Machine learning (ML) has led to disruptive innovations in many fields, such as medical diagnoses. A key enabler for ML is large training data, but existing data, such as medical data, are not fully exploited by ML because of data silos and privacy concerns. Federated learning (FL) is a promising distributed learning paradigm to address this problem. On the other hand, existing FL approaches are vulnerable to poisoning attacks or privacy leakage from a malicious aggregator or client. This article proposes an auditable FL scheme with Byzantine robustness against the aggregator and client: The aggregator is malicious but available, and the client could perform poisoning attacks. First, the Pedersen commitment scheme (PCS) for homomorphic encryption was applied to preserve privacy and for commitments to the FL process to achieve auditability. The auditability enables clients to verify the correctness and consistency of the entire FL process and to identify parties that misbehave. Second, an efficient technique of divide and conquer was designed based on PCS to allow parties to cooperate and securely aggregate gradients to defend against poisoning attacks. This technique enables clients to share no common secret key and cooperate to decrypt ciphertext, guaranteeing a client’s privacy even if some other clients are corrupted by adversaries. This technique was optimized to tolerate the dropout of clients. This article reports a formal analysis concerning privacy, efficiency, and auditability against malicious participants. Extensive experiments on various benchmark datasets show that the scheme is robust with high model accuracy against poisoning attacks.

Abstract We consider medical image transformation problems where a grayscale image is transformed into a color image. The colorized medical image should have the same features as the input image because extra synthesized features can increase the possibility of diagnostic errors. In this paper, to secure colorized medical images and improve the quality of synthesized images, as well as to leverage unpaired training image data, a colorization network is proposed based on the cycle generative adversarial network (CycleGAN) model, combining a perceptual loss function and a total variation (TV) loss function. Visual comparisons and experimental indicators from the NRMSE, PSNR, and SSIM metrics are used to evaluate the performance of the proposed method. The experimental results show that GAN-based style conversion can be applied to colorization of medical images. As well, the introduction of perceptual loss and TV loss can improve the quality of images produced as a result of colorization better than the result generated by only using the CycleGAN model.

Crowdsourcing for Human Intelligence Tasks (HIT) has been widely used to crowdsource human knowledge, such as image annotation for machine learning. We use a public blockchain to play the role of traditional centralized HIT systems, such that the blockchain deals with cryptocurrency payments and acts as a trustworthy judge to resolve disputes between a worker and a requester in a decentralized setting, preventing false-reporting and free-riding. Our approach neither uses expensive cryptographic tools, such as zero-knowledge proofs, nor sends the worker's answers to the blockchain. Compared with prior works, our approach significantly reduces on-chain cost: it only requires O(1) on-chain storage and O(log N ) smart contract computation, where N is the question number of a HIT. Additionally, our approach uses known answers or gold standards to determine the worker's answer quality. To motivate the requester to use honest known answers, the requester cannot learn the worker's answers if the answer quality does not meet the requirement. We further provide formal security definitions for our decentralized HIT and prove security of our construction.

Complex Event Processing (CEP) over tactical moving objects would detect threats in real time and response defense timely. The input data are stream data collected by radar, sonar or other sensors. From the beginning of preprocessing the input data to the end of threat detection and defense, the system of CEP goes through a series of complicated procedures, working based on the rules to be defined dynamically by users. In this paper, we design and implement a rule-based CEP system over tactical moving objects that allows users to insert, update and delete rule specifications dynamically without code programming.