Truth discovery has received considerable attention in mobile crowdsensing systems. In real practice, it is vital to resolve conflicts among a large amount of sensory data and estimate the truthful information. Although truth discovery has been widely explored to improve aggregation accuracy, numerous security and privacy issues still need to be addressed. Existing schemes either do not guarantee the privacy of each participating user, or fail to consider practical needs in crowdsensing systems. In this paper, we present two reliable and privacy-preserving truth discovery schemes for different scenarios. Our first design is fit for applications where users are relatively stable. By employing the homomorphic Paillier encryption, one-way hash chain, and super-increasing sequence techniques, this approach not only guarantees strong privacy, but also is highly efficient and practical. Our second design suits applications where users are frequently moving. In such an application, we explore data perturbation and homomorphic Paillier encryption to shift all user workloads to the server side, without compromising users' privacy. Through detailed security analysis, we demonstrate that both schemes are secure, practical, and privacy-preserving. Moreover, extensive experiments based on real world and simulated mobile crowdsensing systems, we demonstrate the efficiency of our proposed schemes.

The integration of wearable wireless devices and cloud computing in e-health systems has significantly improved their effectiveness and availability. Patients can upload their personal health information (PHI) files to the cloud, from where the health service providers (HSPs) can obtain appropriate information to determine the health state. This system not only reduces the costs associated to healthcare, but also provides timely diagnosis to save lives. However, a number of privacy concerns arise while sharing sensitive information. In this work, we propose a novel privacy-preserving patient health information sharing scheme, which allows HSPs to access and search PHI files in a secure yet efficient manner. We make use of the searchable encryption technique with keyword range search and multi-keyword search. The proposed privacy-preserving equality test protocol allows different types of numeric comparison searches on encrypted data. We also use a variant of Bloom Filter and message authentication code to classify PHI files, filter false data, and check integrity of search results. Simulations on real world and synthetic data show the feasibility and efficiency of the system, and security analysis proves the privacy-preservation properties.

In mobile crowdsensing, location-based task recommendation requires each data requester to submit a task-related geometric range to crowdsensing service providers such that they can match suitable workers within this range. Generally, a trusted server (i.e., database owner) should be deployed to protect location privacy during the process, which is not desirable in practice. In this paper, we propose the location privacy-preserving task recommendation (PPTR) schemes with geometric range query in mobile crowdsensing without the trusted database owner. Specifically, we first propose a PPTR scheme with linear search complexity, named PPTR-L, based on a two-server model. By leveraging techniques of polynomial fitting and randomizable matrix multiplication, PPTR-L enables the service provider to find the workers located in the data requester’s arbitrary geometric query range without disclosing the sensitive location privacy. To further improve query efficiency, we design a novel data structure for task recommendation and propose PPTR-F to achieve faster-than-linear search complexity. Through security analysis, it is shown that our schemes can protect the confidentiality of workers’ locations and data requesters’ queries. Extensive experiments are performed to demonstrate that our schemes can achieve high computational efficiency in terms of geometric range query.

Vehicle platoon, a novel vehicle driving paradigm that organizes a group of vehicles in the nose-to-tail structure, has been considered as a potential solution to reduce traffic congestion and increase travel comfort. In such a platoon system, head vehicles’ performances are usually evaluated by user vehicles’ feedbacks. Selection of an appropriate and reliable head vehicle while not disclosing user vehicles’ privacy has become an interesting problem. In this article, we present a trust-based and privacy-preserving platoon recommendation scheme, called TPPR, to enable potential user vehicles to avoid selecting the malicious head vehicles. The basic concept of TPPR is that each user vehicle holds a trust value, and the reputation score of the head vehicle is calculated via a truth discovery process. To preserve vehicles’ privacy, pseudonyms and Paillier cryptosystem are applied. In addition, novel authentication protocols are designed to ensure that only the valid vehicles (i.e., the vehicles holding the truthful trust values and joining the vehicle platoon) can pass the authentication. A comprehensive security analysis is conducted to prove that the proposed TPPR scheme is secure against several sophisticated attacks in vehicular ad hoc networks. Moreover, extensive simulations are conducted to demonstrate the correctness and effectiveness of the proposed scheme.

The convenience of using private cars has an accompanying parking challenge which becomes a significant issue in congested metropolitans and downtown areas. The explosive increase in the number of vehicles has substantially raised the issue of finding a suitable parking spot, which is both time and resource consuming. At the same time, many private parking spots remain idle, while their owners are not present at home. To promote the utility of private parking spots and mitigate parking issues, smart parking apps can be used. Unfortunately, some of them suffer from privacy issues that affect participation willingness, while others work in a centralized environment where the availability of service is not guaranteed in the presence of malicious users. In this work, we propose Blockchain-based Smart parking with Fairness, reliability and Privacy protection, called BSFP. Specifically, group signatures, bloom filters, and vector-based encryption are leveraged to protect the user's privacy. The decentralized nature of blockchain is utilized to achieve reliability in smart parking, and the smart contract is used to realize fairness. Comprehensive security analysis and experimental results based on the real-world dataset show that BSFP achieves fairness, reliability and privacy protection with high efficiency.