The outbreak of COVID-19 has exposed the privacy of positive patients to the public, which will lead to violations of users’ rights and even threaten their lives. A privacy-preserving scheme involving virus-infected positive patients is proposed by us. The traditional ciphertext policy attribute-based encryption (CP-ABE) has the features of enhanced plaintext security and fine-grained access control. However, the encryption process requires the high computational performance of the device, which puts a high strain on resource-limited devices. After semi-honest users successfully decrypt the data, they will get the real private data, which will cause serious privacy leakage problems. Traditional cloud-based data management architectures are extremely vulnerable in the face of various cyberattacks. To address the above challenges, a verifiable ABE scheme based on blockchain and local differential privacy is proposed, using LDP to perturb the original data locally to a certain extent to resist collusion attacks, outsourcing encryption and decryption to corresponding service providers to reduce the pressure on mobile terminals, and deploying smart contracts in combination with blockchain for fair execution by all parties to solve the problem of returning wrong search results in a semi-honest cloud server. Detailed security proofs are performed through the defined security goals, which shows that the proposed scheme is indeed privacy-protective. The experimental results show that the scheme is optimized in terms of data accuracy, computational overhead, storage performance, and fairness. In terms of efficiency, it greatly reduces the local load, enhances personal privacy protection, and has high practicality as well as reliability. As far as we know, it is the first case of applying the combination of LDP technology and blockchain to a tracing system, which not only mitigates poisoning attacks on user data, but also improves the accuracy of the data, thus making it easier to identify infected contacts and making a useful contribution to health prevention and control efforts.

Spatial crowdsourcing, e.g., Vehicular Ad-Hoc Network (VANET)-based spatial crowdsourcing, is a new distributed computing paradigm, in which task assignments highly rely on the wisdom of the crowdsourcing platform. However, with the increase in user data leakage incidents, the existing task assignment methods are no longer sufficient to meet the privacy requirements of users. In the existing VANET-based spatial crowdsourcing, task assignments are usually performed by a trusted third party based on the real locations of tasks and drivers (task performers), which may lead to the leakage of the users' locations. Furthermore, the drivers usually prefer to query the nearest tasks to them in a geometric range, at this point sending query requests to remote crowdsourcing servers increases unnecessary response delays. To assign tasks securely and efficiently, we propose a privacy-preserving task assignment scheme based on OT and edge computing (PriTAEC), which is the first to apply Oblivious Transfer (OT) and edge computing to preserve the location privacy of VANET-based spatial crowdsourcing. In the scheme, we first utilize Hilbert Curve and Bloom Filter to implement location range queries. Then, we use geohash location encoding and Oblivious Transfer to achieve fine-grained location matching. In particular, we design a task assignment algorithm with an offline-online phase to improve the efficiency of task assignments. Finally, we prove the security of the scheme and evaluate its performance, which shows our scheme is secure and efficient.

The explosions of mobile communications and the Internet of Things (IoT) have spawned a new distributed computing paradigm—spatial crowdsourcing, in which workers actively participate in spatiotemporal computing tasks for earning commissions, facilitating the development of urban sharing economic services. Furthermore, to reduce users’ storage space and computational overhead, the server assignment model (SAM) is widely used, which means that crowdsourcing platforms collect sensitive information about tasks and workers, e.g., locations and interests, to perform task assignments accurately. However, in the real world, crowdsourcing platforms are not fully trustworthy and may reveal sensitive information about workers and tasks, which can reduce users’ motivation to use crowdsourcing services. Therefore, how to assign tasks efficiently and securely is still an urgent problem to be solved. In this article, we propose a privacy-preserving task assignment scheme (PPTA), in which the crowdsourcing platform efficiently implements the nearest task assignments without revealing sensitive information about tasks and workers. In PPTA, we utilize inner product functional encryption to achieve circular range queries and multikeyword queries. Considering that workers usually prefer to query the nearest tasks for reducing travel costs, we use the grid location intersection to enable the nearest task assignment. In particular, we design a SAM algorithm, which can improve task assignment rates in multitask and multiworker scenarios. In addition, our scheme can implement user accountability and user revocation, which enhances the security and practicality of the scheme. Finally, we demonstrate the privacy preservation through security theoretical proofs and show the efficiency by constructing extensive comparative experiments, which respectively illustrate the security and the effectiveness of our scheme.

With 5G and Internet technologies developing rapidly, outsourcing images to cloud servers has attracted growing attention. In existing technologies, images are often outsourced to cloud servers to reduce storage and computing burdens. However, outsourcing images to cloud servers without any processing may reveal the users' privacy, because the images may contain sensitive information about users, such as faces and locations, especially in electronic investigation. To overcome the security problems in image retrieval, we propose a privacy-preserving image retrieval scheme based on deep convolutional neural network (DCNN) and vector homomorphic encryption (VHE). We adopt DCNN and hash algorithms to extract image feature vectors, which improves retrieval accuracy. By combining VHE and K-means outsourcing clustering algorithms, the cloud server can build encrypted index trees, which speeds up the search and reduces the computational cost. In addition, a lightweight access control technique is used to allow image owners to set access policies for datasets flexibly. We prove the security of the proposed scheme and show the effectiveness of the scheme through experiments. Our scheme is suitable for application in electronic image investigation systems (EIIs) to optimize the storage and search of police data.

Attribute-based Searchable Encryption (ABSE) schemes allow keyword encryption to be outsourced to cloud servers, and users can securely search for keyword ciphertext documents of interest. However, most existing searchable encryption schemes do not encourage the fine-grained search of users, and attributes are exposed to third parties easily. Moreover, ABSE schemes have high computational costs for resource-constrained clients on the user's client side. To solve the above problems, a verifiable and multi-keyword searchable encryption scheme based on linear secret sharing schemes (LSSS) is presented by us, which enables users to achieve a more efficient and secure fine-grained search of multi-keyword documents. In this scheme, access control is achieved using LSSS technology, and user attribute hiding is accomplished by hash. Furthermore, user authentication and pre-decryption are outsourced to cloud servers without leaking any information, which lessens the client's computational burden. It is demonstrated that the scheme can resist selective plaintext and selective keyword attacks in security models. In addition, the performance evaluation shows that our scheme performs well in real application scenarios.

Secret sharing (SS) technology is widely used in multi-party computation (MPC) because of its simple secret structure and low computational overhead. Aiming at solving the problem that the existing SS-MPC scheme cannot solve the collusion of all computing parties to recover the original data, this work designs a practical SS-MPC for collusion whole (PSCW) scheme. Through the negotiation of redundant computing nodes and redundant security parameters between computing parties, PSCW scheme can ensure that original data cannot be recovered even if all computing nodes collude to obtain all secret shares, which guarantees the confidentiality of the scheme. At the same time, with the addition of redundant computing nodes, PSCW scheme can correctly realize computing requirements of users and ensure the correctness of the calculation results. Compared with the existing SS-MPC scheme, PSCW scheme has higher security and higher application value in real scenes.