With the advancement of smart terminals and communication technologies, the emergence of heterogeneous Service Subscribers (SSs) and diverse sensing demands has facilitated the development of multi-platform Mobile CrowdSensing (MCS) scenarios. However, unlike traditional single-platform scenarios, Mobile Users' (MUs) bidding privacy is hard to protect in multi platform MCS. Additionally, the privacy disclosure issue of SSs has not been well addressed. To tackle these issues, in this paper, we propose a bilateral, auction-based scheme to preserve bidding privacy in multi-platform MCS, thereby protecting the interests of both SSs and MUs. Specifically, since SSs and MUs strategically choose one another to maximize their utility, we construct the corresponding selection processes for both sides by taking advantage of auction pricing theory. We firstly design a user-oriented forward auction that integrates the 0-1 knapsack problem with the Paillier encryption algorithm to protect the bidding information of both SSs and MUs. Then, we employ the Chinese Remainder Theorem (CRT) to design a reverse auction that hides the bidding behaviors of MUs. Theoretical analysis demonstrates that our scheme can protect the bidding privacy of both parties while ensuring economic robustness. Extensive experiments on a real dataset demonstrate that, compared with existing works, our scheme enables both SSs and MUs to achieve satisfactory utility while maintaining low computational overhead.

As a typical application in Industrial Internet of Things (IIoT), Location-Based Service (LBS) are experiencing rapid development. To address the issue of user location privacy leakage during LBS, distributed spatial cloaking attracts considerable attention from researchers. This approach not only provides users with precise query results but also enhances their control over personal information, allowing users to independently decide whether to participate in constructing Anonymous Cloaking Regions (ACRs). However, existing LBS schemes are unable to effectively prevent malicious behavior by requesters or cooperators, leading to user privacy leakage. To resolve this issue, we propose a novel privacy-preserving LBS scheme that incorporates a Certified Reputation (CR) mechanism. The proposal integrates user-owned CRs with preset trust thresholds, forming an innovative interactive verification mechanism. This mechanism enhances user privacy while establishing a trustworthy framework, within which each user individually manages their CR information. It not only adapts to fully distributed scenarios but also supports cross-regional trust evaluation. Meanwhile, the scheme establishes trust relationships among users, ensuring that only mutually trusted users can collaborate to construct ACRs. Security analysis and extensive experiments demonstrate that the proposed scheme effectively regulates user behavior during the construction of ACRs. Furthermore, it imposes minimal computational and communication overhead, highlighting its efficiency and practicality.

Blockchain-based distributed mobile crowdsensing (MCS) has been widely adopted in areas, such as the Industrial Internet of Things (IIoT) to transform traditional data collection methods. However, existing works lack a theory-driven quantitative utility analysis and precise reward and punishment measures that effectively deter malicious behavior. This deficiency hinders the ability to constrain rational participants’ behavior, thereby compromising the trustworthiness of MCS implementations. To address the issue above, this article proposes Crowd-BT: a bilateral trustworthy ensured scheme for blockchain assisted MCS. Specifically, Crowd-BT first devises reward and punishment measures based on participants’ short-term utility analysis for an unrestricted context in the ideal case. Then, for the more practical situation, Crowd-BT designs blocklist punishment measures for participants, ensuring self-containment based on long-term utility analysis. Finally, leveraging the practical blocklist punishment measures and the devised anonymity behavior record certificates (ABRCs) using the message-hidden signature, Crowd-BT details the implementation steps for MCS under the blockchain. Theoretical analysis shows that Crowd-BT achieves both utility and security goals. Extensive experiments illustrate that Crowd-BT effectively constrains participant behavior. Compared to the existing works, it significantly enhances the trustworthiness of MCS.

Recruiting users in mobile crowdsensing (MCS) can make the platform obtain high-quality data to provide better services. Although the privacy leakage during the process of user recruitment has received a lot of research attention, none of the existing work considers the evaluation of the sensing quality of privacy-preserving data submitted by users, which makes the platform incapable of recruiting users suitably to obtain high-quality sensing data, thereby reducing the reliability of MCS services. To solve this problem, we first propose a sensing quality evaluation method based on the deviation and variance of sensing data. According to it, the platform can obtain the sensing quality of privacy-preserving data for each user during the recruitment. Then we model the user recruitment with a limited budget platform as aCombinatorial Multi-Armed Bandit (CMAB)game to determine the recruited users based on the sensing quality of data obtained by evaluation. Finally, we theoretically prove that our algorithm satisfies differential privacy and the upper bound on theregretof rewards is restricted. Experimental results show that our proposal is superior in various properties, and our method has a 73.67% advantage in accumulated sensing qualities compared with comparison schemes.

To motivate data owners’ (DOs’) trading willingness, the existing incentive mechanisms allow DOs to independently disturb data following data consumer's (DC’s) availability requirement. However, they cannot motivate DOs’ honest disturbance, which is attributed to DOs’ independent disturbance without any supervision. Thus, we implement an incentive mechanism for privacy preserved data trading with verifiable data disturbance where an honest-but-curious disturbance generator (DG) is additionally introduced to supervise DOs’ local disturbance and assist disturbance verification between DOs and DC. Specifically, DG generates the disturbance strategies and secretly distributes to DOs following private information retrieval, guaranteeing DOs's local disturbance's privacy and verifiability with our proposed three-level verification algorithm. Subsequently, we model the trading as a game and disturbance verification results determine the compensation and punishment for trading bilateral utilities following Nash Equilibrium where DOs honestly disturb data. Theoretical analysis shows that DOs are motivated to honestly disturb data and their raw data privacy is preserved. Extensive experiments using the real-world dataset demonstrate that the deviating DOs in our scheme can be verified with a probability of more than 90% and the statistical result accuracy can be improved by more than 80% compared with the existing works.

Perturbation-based privacy-preserving truth discovery requires the Service Provider (SP) to calculate the truthful aggregation result from perturbed data of the Data Sources (DSs), which inevitably damages the aggregation accuracy due to perturbation noise added in the data. Thus, the existing works attempt to relieve the perturbation errors by reducing noise amounts or adjusting aggregation weights of DSs. However, the former sacrifices DSs' privacy preservation and the latter has the limited accuracy recovery performance. Aiming at it, we propose an accuracy-enabling differential privacy-preserving truth discovery consisting of an independence-guaranteed data perturbation module and a progressive-private noise elimination module. Specifically, in the first module, SP generates mass of noises following DS's desired perturbation parameters and DS privately obtains one of noise based on private information retrieval. Meanwhile, to realize the perturbation's traceability, SP preserves the ciphertext of DSs' acquired noises, assisting the following noise elimination. In the second module, SP first removes his preserved DS's encrypted noise from perturbed truth according to homomorphic encryption, and then requires DS to decrypt this cleaned truth. The above two processes are progressively and iteratively implemented until all DSs have been involved. Theoretical analysis shows that our scheme can protect DSs' raw data privacy in both truth discovery process and noise elimination process. Extensive experiments using the real-world dataset demonstrate that our scheme can effectively eliminate more than 90% of the perturbation noise effects on the truth discovery accuracy.

To motivate data owner (DO) to trade data, the existing data trading allows DO to sell the disturbed data to the data consumer (DC), where the disturbance parameter and the data price are negotiated by them, and DO independently adds the disturbance noise to data (usually continuous type) following the negotiation result. However, DOs may violate the negotiated parameter and add more noise to data while obtaining the negotiated price, which damages DC's disturbed data availability. This deficiency is rooted in the absence of supervision and verifiability on DOs' independent disturbances. Aiming at the above problem, we devise a privacy-preserved data trading via verifiable data disturbance. Specifically, the honest-but-curious disturbance server (DS) is introduced to generate encrypted verifiable disturbance noises, and secretly distribute noises to DOs referring to the method of private information retrieval. Using homomorphic encryption, DOs finish data disturbance without knowing noises' specific sizes. Subsequently, DC selects DOs to verify with our proposed anti-forgery verification, where the anti-forgery on both disturbance noise and original data guarantees verification correctness. Theoretical analysis proves that DOs' original data is preserved in data trading. Extensive experiments using the real-world dataset demonstrate that our scheme can detect more than 80% of malicious DOs and decrease their utilities to punish malicious disturbance compared with existing works.

The advanced data trading allows data generator’s (DG) disturbed data to be traded as both initial and reselling trading modes, which meets DG’s raw data privacy and data consumers’ (DCs) vast data requirement. However, the traded data truthfulness verifiability cannot be guaranteed in the privacy-preserved way. Firstly, due to DG’s independent and random disturbance, DC cannot verify whether the traded data is disturbed under his required disturbance parameter without carrying privacy leakage on DG. Secondly, because the reselling trading is allowed, DC can hardly verify the traded data’s origin truthfulness under the deceiving of data reseller (DR) while protecting his purchase privacy. Aiming at the above problems, we propose the privacy-preserved data disturbance and truthfulness verification for data trading. Specifically, an honest-but-curious trading server (TS) is introduced to assist our devised private-verifiable imprint-embedded disturbance method where imprint is blinding. Subsequently, TS implements the adaptive truthfulness verification by constructing imprint-embedded individual verification formula and requiring verified participants to decrypt the formula result. The verified participants cannot inform the blinding imprint value to forge the correct result, ensuring the accuracy of the devised verification method. Theoretical analysis proves that participants’ privacy is preserved and the traded data’s truthfulness can be guaranteed. Extensive experiments using the real-world dataset demonstrate that without any extra privacy cost, our scheme verifies 100% untruthful traded data compared with the existing solutions’ 50%.

When enjoying mobile crowdsensing (MCS), it is vital to evaluate the trustworthiness of mobile users (MUs) without disclosing their sensitive information. However, the existing schemes ignore this requirement in the multiple crowdsourcers (CSs) scenario. The lack of a credible sharing about MUs’ trustworthiness results in an inaccurate trust evaluation, disabling allocating tasks to reliable MUs. To address it, based on the analysis of the desired properties, we propose a scheme enablingdependabledata collection withmultiple crowdsourcerstrustsharing ($D^{2}MTS$). Specifically, we design the MU anonymous management. Two kinds of MU generated pseudonym systems without relationships are presented to mark each MU in trust evaluation and task execution, respectively. Through the devised pseudonym changes on these pseudonyms and the common token distribution algorithm,$D^{2}MTS$realizes privacy-preserving trust sharing. Moreover, to guarantee credible sharing, based on the hash chain,$D^{2}MTS$records MUs’ trustworthiness with the unforgeable signature on the blockchain established by multiple CSs which do not trust each other naturally. Extensive experiments show that compared with the other works,$D^{2}MTS$'s detection ratio of vicious MUs and the percentage of reliable MUs among the selected ones can increase by 208.61% and 28.27%. Both computational and communication delays are limited.

To motivate users' assistance for protecting others' location privacy by distributedK-anonymity in Location-Based Service (LBS), many incentive mechanisms have been proposed, where users obtain monetary compensation for their assistance. However, most existing distributedK-anonymity incentive mechanisms rely on trusted third parties and ignore users' malicious strategies, which destroys LBS's distributed structure as well as leads to users' privacy leakage and incentive ineffectiveness. To solve the above problems, we propose aPrivacy-Enhanced incentive mechAnism for distributedK-anonymity (PEAK). With determining the monetary transaction relationship and location transmission between users, PEAK enables the anonymous cloaking region construction without the trusted server. Meanwhile, PEAK devises role identification mechanism and accountability mechanism to restrain and punish malicious users, which protects users' location privacy and implements effective motivation on users' assistance. Theoretical analysis based on the game theory shows that PEAK constrains users' malicious strategies while satisfying individual rationality, computational efficiency, and satisfaction ratio. Extensive experiments based on the real-world dataset demonstrate that PEAK improves security and feasibility, especially reaching the success rate of anonymous cloaking region construction to more than 90$\%$and decreasing the malicious users' utilities significantly.

Mobile crowdsensing (MCS) has been applied in various fields to realize data sharing, where multiple platforms and multiple Mobile Users () have appeared recently. However, aiming at mutual selection, the existing works ignore making ’ utilities with the limited resources and platforms’ utilities while achieving the desired sensing data quality maximum as far as possible. Thus, they cannot motivate both and platforms to participate. To address this problem, standing on both sides of and platforms with conflicting interests, we propose a Progressive two-stage Auction-based Multi-platform Multi-user Mutual Selection scheme (). Specifically, in, we treat mutual selection as a two-stage auction and devise the auction models for and platform using forward and reverse auction ideas, presenting and maximizing the utilities from their respective perspectives. Then, based on the proposed progressive two-stage auction structure, we adopt 0-1 knapsack and Myerson’s price theory to construct the first stage -oriented auction and the second stage platform-oriented auction, achieving devised models. Theoretical analysis shows that is economically robust. Extensive experiments on the real dataset demonstrate that respectively promotes platforms’ and ’ utilities by 76.23% and 10.74 times, compared with the existing works.

To motivate users to submit high quality data for mobile crowdsensing (MCS), some quality-aware incentive mechanisms have been proposed, which recruit and pay users strategically. However, in the existing mechanisms, the recruitment based only on tasks matching degree leads to the ineffective insistent data quality incentive. Meanwhile, the absence of the reasonable payment strategy cannot motivate users to submit high quality data in the current task. To address the above problems, we propose anOnlineall-phase quality-awareincentive mechanism (Oasis) to realize the quality incentive in both recruitment and payment phases. With the knapsack secretary, Oasis first devises a quality-aware pre-budgeting recruitment strategy, which decides whether the arriving user's long-term data quality and bid satisfy the recruited criterion. Then, in the payment phase, Oasis evaluates and updates the current and long-term data qualities of users. Based on the evaluation results, a two-level payment strategy is devised employing the Myerson theorem, where users submitting higher quality data can obtain more utilities under the budget constraint. Theoretical analysis proves that Oasis satisfies economic feasibility and constant competitiveness while achieving quality incentive in recruitment and payment phases. Extensive experiments using the real-world dataset demonstrate that the sensing result accuracy of Oasis increases 67% compared with the existing works.

In unmanned aerial vehicle (UAV) performing tasks, the UAV often faces electricity shortages. The traditional scheme to charge a UAV needs to return to the ground. Using the charging UAV (CUAV) can avoid the waste of electricity caused by the return. However, the existing works only consider a fixed charging location for electricity replenishment. Moreover, fewer works focus on the matching relationship between multi-CUAV and multi-UAV. It is challenging to complete the expected charging work due to the mismatch between the electricity demand and supply. To address this problem, we propose a two-stage electricity scheduling scheme. Specifically, in the charging location selection stage, we solve the Nash equilibrium (NE) of flight consumption between CUAVs and UAVs through the exact potential game, thereby determining the accessible charging position. Then, in the electricity transaction stage, we adopt the Stackelberg game model to determine the Stackelberg equilibrium (SE) between the acceptance rate of CUAVs and the rejection rate of UAVs, ensuring that both CUAVs and UAVs are satisfied with the unit electricity prices and electricity demands. Based on the above two game stages, we propose a supply and demand scheduling (SDS) algorithm to achieve dynamic scheduling between CUAVs and UAVs. Theoretical analysis indicates the exits of NE and SE. Furthermore, the extensive experiments show that our scheme has significant advantages over the baselines in charging cost, charging price, and flight consumption.

Distributed spatial cloaking (DSC) enables users to enjoy precise location-based service (LBS) with location privacy preserving. An incentive mechanism is necessary to encourage users to cooperate. However, due to the inappropriate design of incentive mechanisms, the existing works cause low user benefits and fail to encourage users, ruining the expected incentive effect. Moreover, introducing a third party to manage users’ information also causes the existing works to disclose users’ privacy and be unpractical. To address these issues, we propose a utility-aware incentive mechanism-based distributed spatial cloaking (RESAT). By the idea of utility theory and optimization theory, RESAT devises basic and extended incentive mechanisms. The two mechanisms for assuming that all users are honest and that malicious users provide unreasonable locations. RESAT proposes an incentive mechanism-based cloaking cooperation without a third party, incorporating the developed mechanisms based on the blind signature. Theoretical analysis indicates that RESAT achieves incentive compatibility and is secure. Extensive experiments on the real data set show that compared with the existing works, RESAT enables 1 time more users to cooperate at best while eliminating the malicious behaviors that provide unreasonable locations. The required DSC construction time delay is limited.

Acquiring the spatial distribution of users in mobile crowdsensing (MCS) brings many benefits to users (e.g.,avoiding crowded areas during the COVID-19 pandemic). Although the leakage of users’ location privacy has received a lot of research attention, existing works still ignore the rationality of users, resulting that users may not obtain satisfactory spatial distribution even if they provide true location information. To solve the problem, we employ game theory with incomplete information to model the interactions among users and seek an equilibrium state through learning approaches of the game. Specifically, we first model the service as a game in the satisfaction form and define the equilibrium for this service. Then, we design aLEFSalgorithm for the privacy strategy learning of users when their satisfaction expectations are fixed, and further designLSREthat allows users to have dynamic satisfaction expectations. We theoretically analyze the convergence conditions and characteristics of the proposed algorithms, along with the privacy protection level obtained by our solution. We conduct extensive experiments to show the superiority and various performances of our proposal, which illustrates that our proposal can get more than 85% advantage in terms of the sensing distribution availability compared to the traditional spatial cloaking based solutions.

Nowadays, the pseudonym swap has become the mainstream technology for protecting vehicles' trajectory privacy in vehicle ad hoc networks. However, the existing pseudonym swap methods cannot strictly provide the unlinkability between the new pseudonym and old pseudonym of the vehicle due to the lack of theoretical privacy guarantee, resulting in severe leakages of vehicles' trajectory privacy. Our experiment also proves this point and we find that existing works may cause vehicle's pseudonyms to be linked with a probability higher than 60% because they always choose two vehicles with very different driving states (e.g., speeds, directions, and positions) to swap their pseudonyms. To solve this issue, we first give a formal privacy definition based on generalized differential privacy, called pseudonym indistinguishability, to provide a strict unlinkability for pseudonym swap. Then, we design an appropriate utility metric and a new pseudonym swap mechanism, which selects a pseudonym for a vehicle by adapting a differential privacy exponential mechanism to satisfy pseudonym indistinguishability. Abstracting from attackers' prior knowledge, we can strictly guarantee that if two vehicles have a high similarity of driving states, it is impossible for attackers to link the vehicles and their pseudonyms after the swap. Theoretical analyses prove that our mechanism satisfies the proposed privacy definition, thus ensuring the unlinkability between the new pseudonym and the old pseudonym. Extensive experiments on a real data set show that our work only requires about 50% of pseudonym quantities compared to other works and can make the vehicle successfully complete the swap process with a probability of more than 90%, which is higher than any of existing works.

Summary The deblocking filter inhigh‐efficiency video coding(HEVC) has huge computational complexity because of its high content‐adaptive coding structure as well as high‐definition. Parallelization for it based on massively parallel architectures such asgraphics processing unitbecomes an urgent demand. However, a large number of conditional branches and data dependencies severely hinder its efficient parallelization. In this paper, a novel parallel optimization strategy based on graphics processing unit is presented for concurrent deblocking in HEVC/H.265 standard to improve the parallel performance. First, by reducing various conditional branches, a normalization mechanism for instruction stream based on feature vector is proposed, which improves the efficiency of boundary strength computation dramatically. The idea can also be applied to edge discrimination. Second, a parallel mechanism based on an adaptive post‐correction is presented to process vertical and horizontal edges filtering concurrently, which improves the processing speed obviously, while producing negligible quality loss. Experimental results show that the strategy presented outperforms the existing state‐of‐the‐art method with accelerating factor up to 32. Copyright © 2016 John Wiley & Sons, Ltd.