The IoT devices deployed in various application scenarios will generate massive data with immeasurable value every day. These data often contain the user’s personal privacy information, so there is an imperative need to guarantee the reliability and security of IoT data sharing. We proposed a new encrypted data storing and sharing architecture by combining proxy re-encryption with blockchain technology. The consensus mechanism based on threshold proxy re-encryption eliminates dependence on the third-party central service providers. Multiple consensus nodes in the blockchain network act as proxy service nodes to re-encrypt data and combine converted ciphertext, and personal information will not be disclosed in the whole procedure. That eliminates the restrictions of using decentralized network to store and distribute private encrypted data safely. We implemented a lot of simulated experiments to evaluate the performance of the proposed framework. The results show that the proposed architecture can meet the extensive data access demands and increase a tolerable time latency. Our scheme is one of the essays to utilize the threshold proxy re-encryption and blockchain consensus algorithm to support IoT data sharing.

Online Ride-Hailing(ORH) service enables riders to enjoy on-demand transportation service with mobile devices. Despite the convenience of the service, privacy leakage could be caused because riders need to submit their locations during ride matching. To protect users' privacy, much effort has been done to construct a privacy-enhanced ORH system in recent years. However, almost all of current privacy-preserving schemes match the nearest driver to the requesting rider, it is not the optimal matching strategy and unnecessary waste of travel distance may be caused in this way. To solve the problem, we propose a privacy-preserving ORH scheme named pRide, which minimizes the overall travel distance by matching the best driver in global perspective instead of the nearest driver in the local region. To find the best driver, we utilize a deep learning model to predict emergence of ride requests in various regions, and enables the ORH server to perform ride matching leveraging prediction results. Based on the framework, we also propose optimizations to enhance pRide. Moreover, to further defend inference attacks from riders, we propose a private comparing algorithm at the cost of a little communication overhead. Through theoretical analysis and experiments, we prove that pRide is able to decrease the overall travel distance securely and efficiently in ORH matching scenarios.

A staggering number of consumer Internet-of-Things devices are being deployed in various application scenarios, and massive data will be generated per day. How to achieve a secure and efficient data-sharing scheme for the consumer IoT applications is a huge challenge for us. The traditional cloud-based IoT has the dilemma of prolonged communication delay and privacy leakage. With the application of 5G technology, edge computing can effectively alleviate these problems. However, it cannot meet the higher security requirements for the data sources’ authenticity and information reliability. By combining the blockchain and smart contracts technology, this article proposes a distributed, efficient, and secure data-sharing scheme centered on consumer IoT devices. This architecture consists of four layers: 1) IoT devices layer, 2) edge storage layer, 3) blockchain network layer, and 4) application services layer. We design smart contracts based on the attributed based access control and the searchable encryption algorithm, including device retrieval contract, policy management contract, and authorization verification contract. Through the implementation of simulated experiments, we prove that our proposed architecture can satisfy the large-scale data access requests and bring a tolerable level of communication overhead. The proposed framework is one of the few attempts to leverage the edge computing and blockchain technologies to support IoT data sharing.

Online ride-hailing (ORH) services allow people to enjoy on-demand transportation services through their mobile devices in a short responding time. Despite the great convenience, users need to submit their location information to the ORH service provider, which may incur unexpected privacy problems. In this paper, we mainly study the privacy and utility of the ride-sharing system, which enables multiple riders to share one driver. To solve the privacy problem and reduce the ride-sharing detouring waste, we propose a privacy-preserving ride-sharing system named pShare. To hide users’ precise locations from the service provider, we apply a zone-based travel time estimation approach to privately compute over sensitive data while cloaking each rider’s location in a zone area. To compute the matching results along with the least-detouring route, the service provider first computes the shortest path for each eligible rider combination, then compares the additional traveling time (ATT) of all combinations, and finally selects the combination with minimum ATT. We designed a secure comparing protocol by utilizing the garbled circuit, which enables the ORH server to execute the protocol with a crypto server without privacy leakage. Moreover, we apply the data packing technique, by which multiple data can be packed as one to reduce the communication and computation overhead. Through the theoretical analysis and evaluation results, we prove that pShare is a practical ride-sharing scheme that can find out the sharing riders with minimum ATT in acceptable accuracy while protecting users’ privacy.

Mobile users’ demands to delay-sensitive video streaming media put forward new requirements for mobile networks, such as architecture optimization. Edge caching as a new paradigm is proposed to enhance the quality of service (QoS) for mobile users at the network edge. Due to the limited coverage of edge cache nodes, the frequent handoffs between base stations would aggravate network traffic overhead, resulting in a bad experience of high latency and service interruption when mobile users browse videos. This paper first proposes a three-layer mobile edge network architecture and applied edge caching to video streams to build an efficient caching system. Given the user’s mobility and low latency of mobile video streaming, we propose an edge caching strategy based on user speed and content popularity. Horizontally, the user’s speed affects the spanning area and the buffer size of the cache on edge; vertically, content popularity determines the priority of cached videos. Experimental results demonstrate that our caching strategy outperforms other schemes in terms of the average delay and the cache hit ratio in mobile video streaming scenes compared with the other three classic caching methods.