With the popularization of smartphones with GPS, user information attached to the locations is facing the risk of leakage. In the real world, even if the locations of a user are well protected, attackers can also mine user privacy by analyzing the user correlations in common hotspots. To solve the above problems, we propose a local differential privacy extension scheme in hotspot areas. In this scheme, we firstly obtain the user’s hotspots by mining the user trajectories based on the sliding time window, and then, we extract the correlation degrees among users from these hotspots using the Jaccard correlation coefficient, and finally we introduce the personalization correlation sensitivity to extend the local differential privacy so as to protect sensitive locations in hotspot areas. Experiments show that, compared with existing methods, our scheme can improve the usability of trajectories up to $\mathbf{6. 6 4 \%}$ at the same privacy level.

With the popularity of mobile terminals with GPS functions, location-based services are widely used, and all kinds of user information attached to the location are facing the risk of disclosure, and privacy protection is being challenged. In current researches, it is usually assumed that the locations of different users are independent of each other. However, in real world, the locations of different users have some certain internal correlation. Even if the locations of a single user are well protected, attackers can still mine user privacy through the correlation analysis of locations. To solve the above problems, this article proposes a multiuser location-correlated differential privacy extension scheme under strict privacy budget. In this scheme, we first extract the user spatiotemporal characteristics by mining the stay points and stay areas from trajectories based on locations with timestamps, and then, we calculate the correlation degree among users using the Jaccard correlation coefficient according to the spatiotemporal characteristics, and further, we realize the adaptive differential privacy protection of different users by introducing the concept of individual correlation sensitivity, and finally, we design the differential privacy extension method to protect sensitive locations in the stay areas. Experimental results show that, compared with the existing methods, our proposed scheme not only can improve the usability of the trajectories after privacy protection, but also can enhance the privacy protection of the sensitive locations in the stay areas.

This paper presents RF-Mehndi, a passive commercial RFID tag array formed identifier. The key RF-Mehndi novelty is that when the user’s fingertip touching on the tag array surface during the communication, the backscattered signals by the tag array become user-dependent and unique. Hence, if we enhance the communication modality of many personal cards nowadays by RF-Mehndi, in case that a card gets lost or stolen, it cannot be used illegally by the adversaries. To harvest such a benefit, we leverage two key observations in designing RF-Mehndi. The first one is when tags are nearby, their interrogated currents can change each other’s circuit characteristics, based on which unique phase features can be obtained from backscattered signals. The second observation is that when the user’s fingertip touches the tag array surface during communication, the phase feature can be further profiled by this user. Based on these observations, the card and its holder can be potentially authenticated at the same time. To transfer the RF-Mehndi idea to a practical system, we further address technical challenges. We implement a prototype system. Extensive evaluations show the effectiveness of RF-Mehndi, achieving excellent authentication performance.

With the rapid development of embedded smart devices, a new data collection paradigm, mobile crowd-sensing (MCS), has been proposed. MCS allows individuals from the crowd to act as sensors and contribute their observation data. However, existing MCS systems are mostly based on third-party platforms, and there is no guarantee that a center is completely credible. In addition, security and privacy issues should not be ignored. During MCS’ execution, the participants’ various information and truth value are usually exposed, and the computation related to data privacy cannot be verified. In this paper, we integrate the blockchain into the MCS scenario to design a blockchain based privacy-preserving quality control mechanism, which prevents data from being tampered with, and denied, ensuring that the reward is distributed fairly. In the new system, we propose a privacy preserving participant selection scheme and the result can be verified (i.e., security against malicious node) without any third-party arbiter. Finally, considering the issues with sensing data privacy and efficiency in the truth discovery process, we propose a new privacy-aware crowdsensing design with iterative truth discovery based on rational secure multi-party computation. The experimental results show that compared to the prior result, the proposed solutions are highly practical and facilitate quality control without violating the participant’s privacy.

In recent years, with the popularity of smartphones, the indoor positioning systems based on mobile crowdsensing (MCS) have gained considerable interest and exploit. However, it is still challenging to construct a largescale indoor positioning system. 1) In indoor positioning model, storage and computing resources are very important. 2) The calibration operation of data label and selection of model parameters require the operation of professionals. 3) User location privacy may be compromise, which greatly affects participant safety and enthusiasm. To solve these problems, our model firstly provides an edge-crowdsourcing indoor localization architecture to improve storage, computing power and response speed. Then, based on manifold regularization, a semi-supervised indoor localization model is determined by an adaptive manner in terms of both similarity and manifold structure, which reduces the workload of the positioning model and improve localization accuracy. In addition, we propose a new privacy-aware indoor localization algorithm based on secure multi-party computation to protect location privacy. Experimental results on real-world datasets show that, compared with the previous methods, our method improves accuracy by 0.87m, and in terms of time overhead of privacy protection, our method reduces the running time of the thousand seconds level.

By allocating tasks to participants, crowdsensing has shown large potential in addressing large-scale data sensing problems. Considering the problem of unfair payment, negative work of participants, and cooperative cheating, how to assess data quality of tasks reliably is an important problem in crowdsensing. Therefore, a lightweight blockchain-based model for data quality assessment is proposed in this article. First, there are two data quality assessment processes in the model. One is implemented in the selection of participants and the other is implemented in data quality assessment. Second, consensus mechanism and smart contracts are redesigned to be suitable for crowdsensing. The lightweight consensus mechanism delegated proof of reputation (DPoR) is proposed in the blockchain-based model instead of proof of work (PoW). Furthermore, three smart contracts, verifiers selection contract (VSC), participants employment contract (PEC), and data verify contract (DVC), are generated to constrain the behaviors of the involved parties. Finally, expectation-maximization (EM) algorithm with multiverifiers is proposed to evaluate the performance of task participants. Experiments on the open data sets Wine Quality show that our new method outperforms the existing methods in improving the quality of sensing task.

This paper presents RF-Mehndi, a passive commercial RFID tag array formed identifier. The key RF-Mehndi novelty is that when the user's fingertip touching on the tag array surface during the communication, the backscattered signals by the tag array become user-dependent and unique. Hence, if we enhance the communication modality of many personal cards nowadays by RF-Mehndi, in case that a card gets lost or stolen, it cannot be used illegally by the adversaries. To harvest such a benefit, we have two key observations in designing RF-Mehndi. The first observation is when tags are nearby, their interrogated currents can change each other's circuit characteristics, based on which unique phase features can be obtained from backscattered signals. The second observation is that when the user's fingertip touches the tag array surface during communication, the phase feature can be further profiled by this user. Based on these observations, the card and its holder can be potentially authenticated at the same time. To transfer the RF-Mehndi idea to a practical system, we further address technical challenges. We implement a prototype system. Extensive evaluations show the effectiveness of RF-Mehndi, achieving excellent authentication performance.

With the popularization of intelligent terminals, crowdsensing has become increasingly prominent because of its advantages, such as low cost, high convenience, and fast speed in conducting tasks. However, the quality of the data collected through crowdsensing is varied and is difficult to evaluate. Furthermore, the existing crowdsensing quality control methods are mostly based on a central platform, which is not completely trusted in reality and results in the existence of fraud and other problems. To solve these two questions, a crowdsensing quality control model based on a two-consensus blockchain is proposed in this paper. First, the idea of a blockchain is introduced into this model. The credit-based verifier selection mechanism and the two-consensus approach are proposed to realize the nonrepudiation and nontampering of information in crowdsensing. Then, to help task publishers obtain higher-quality sensing data, the methods of node matching and QGE are proposed. The former method uses the idea of the calculation of matching degree to select workers, and the latter uses the idea of clustering and fuzzy theories to evaluate the quality of the sensing data. Finally, the experiments show that the running time of the block generation in our model is acceptable, and comparing with the other methods, our model can acquire data of higher ioj.

With the rapid growth of smart terminals in recent years, crowdsensing which utilizes the human intelligence to solve complicated problems have gained considerable interest and exploit. The majority of the existing crowdsensing systems rely on a trusted third-party platform to complete sensing tasks and collect large-scale data. However, the platform cannot completely ensure trust in the real world. The issues of security and privacy caused by the center platform should not be ignored. In this paper, we propose a decentralized privacy-preserving model based on twice verifications and consensuses of blockchain (TCNS). In the prototype of TCNS, an anonymity strategy which can be verified based on the elliptic curve algorithm is proposed to protect the user identity privacy. Then, we propose a twice consensus mechanism, which ensures that the data can be traced and avoids data from being impersonated, tampered with, and denied. Moreover, we propose a user attribute protection scheme based on the lightweight homomorphic encryption algorithm. Finally, considering various influencing factors comprehensively, TCNS uses fuzzy theories to select the candidate mobile nodes. Further, we implement the prototype with real-world datasets, the experimental analysis of privacy protection and safety shows that TCNS can effectively prevent association analysis attacks and background knowledge attacks. More gratifying, the time overhead for generating a new block is acceptable.