Most of the mobile phones have GPS sensors which make location based service (LBS) applicable. LBS brings not only convenience but also location privacy leak to us. Achieving anonymity and sending private queries are two main privacy-preserving courses in LBS. A novel location privacy-preserving method is proposed based on Voronoi graph partition on road networks. Firstly, based on the prediction of a user's moving direction, a cooperative k-anonymity method is proposed without constructing cloaking regions which may lead to efficiency decline in continuous query. And then, a query algorithm is proposed without providing any user's actual location, replaced by continuous anchor sequence, to LBS provider. This algorithm can work out precise results according to candidate sets returned by LBS provider and it also solves uneven distribution problem in SpaceTwist. Performance analysis and experiments show that our method achieves a preferable tradeoff between QoS and location privacy preserving; it has obvious advantages compared with other methods.

With the rapid popularization of current Internet of Things (IoT) technology and 5G networks, as well as the continuous updating of new service lifestyles and businesses, the era of big data processing for the IoT has arrived. However, centralizing all data for processing in the cloud can lead to issues such as communication latency and privacy breaches. To solve these problems, edge computing, as a new network architecture close to terminal data sources and supporting low latency services, has gradually emerged. In this context, cloud edge collaborative computing has become an important network architecture. With the changing security requirements and communication methods of cloud edge collaborative network architecture, traditional authentication key agreement protocols are no longer applicable. Therefore, a new IoT authentication and key agreement protocol needs to be designed to solve this problem. This study proposes an IoT accessible solution for cloud edge collaboration. This scheme adopts a chaotic mapping algorithm to achieve efficient authentication. It ensures the anonymity and untraceability of users. Following this, we conducted strict security verification using BAN logic and Scyther tools. Through experimental comparative analysis, the research results show that the protocol performs better than other schemes while ensuring security. This indicates that the protocol can achieve efficient authentication and key negotiation in cloud edge collaborative network architecture, providing a secure and reliable solution for the accessibility of the IoT.

The emergence of mobile Internet of Things (IoT) has brought much more convenience to our daily life than ever before, modern intelligent vehicles are usually equipped with various mobile IoT components or devices. However, when we are enjoying the convenience from the rapid development of mobile IoT services, our privacy can be misused by attackers in an easier way. In this paper, we devise a query scheme with intelligent vehicle privacy guarantees, which enables a vehicle to acquire accurate query service from the service provider (SP) without providing its explicit private information, such as location or query interest privacy. And more than that, we introduce network coding for the first time to make our scheme applicable to a more sophisticated top-K query by multiple vehicles cooperation in mobile IoT. Furthermore, we also consider the unnecessary reveal issue of service data at SP side since the data is its asset. Performance analysis and experiments verify the validity of our scheme and demonstrate a better accuracy and efficiency compared with existing solutions in mobile IoT scenario.

Federated learning (FL) has emerged as an extremely effective strategy for dismantling data silos and has attracted significant interest from both industry and academia in recent years. However, existing iterative FL approaches often require a large number of communication rounds and struggle to perform well on unbalanced datasets. Furthermore, the increased complexity of networks makes the application of traditional differential privacy to protect client privacy expensive. In this context, the authors introduce FedGM: a method designed to reduce communication overhead and achieve outstanding results in non-IID scenarios. FedGM is capable of achieving considerable accuracy, even with a small privacy budget. Specifically, the authors devise a method to extract knowledge from each client’s data by creating a scaled-down but highly effective synthesized dataset that can perform similarly to the original data. Additionally, the authors propose an innovative approach to applying label differential privacy to protect the synthesized dataset. The authors demonstrate the superiority of the approach over traditional methods by requiring only one communication round and by testing using four classification datasets for evaluation. Furthermore, when comparing the model performance for clients using their method against traditional solutions, the authors find that the approach achieves significant accuracy and better privacy.

The widespread application of location recommendation service brings convenience and social fun to users, but the misuse of user check-in data and location recommendation results can bring serious privacy leakage, because these check-in data and location recommendation results imply private information of users' interests and behavioral hobbies. To address the check-in privacy and recommendation result privacy leakage issues in existing location recommendation schemes, we first propose a location recommendation algorithm based on the similarity of user historical check-ins, and then we devise three location recommendation protocols on ciphertext to protect the privacy of user check-in data and recommendation results based on the homomorphic property of Paillier cryptosystem without assuming any trustworthy entity. These protocols calculate the ciphertexts of recommendation results at the location recommendation server(LSP) without learning any explicit check-in privacy of the user, and it also guarantees that the decryption server(DS) can only implement the decryption operations as requested by the user without being aware of any decrypted recommendation results, so that the privacy of user check-in data and recommendation results are both preserved solidly in our protocols. Although the above recommendation process is carried out without any user's explicit data, it is still effective enough to guarantee the accuracy of recommendation results. Finally, we prove the security, efficiency and accuracy of our scheme by extensive performance analysis.