In the era of big data, advanced data processing devices and smart sensors greatly benefit us in many areas. As for each individual user, data sharing can be an essential part of the process of data collection and transmission. However, the issue of constant attacks on data privacy arouses huge concerns among the public. This work proposes a personalized federated learning method associated with correlated differential privacy for autonomous driving. First, instead of transmitting raw data to the server following collection, a device that employs federated learning can perform calculations to obtain the training model at each node. Second, we specifically perform a correlated classification analysis to encrypt data that share high relevance, which can minimize the system cost. Then, correlated differential privacy is utilized to achieve the preservation of data privacy before sharing. In contrast to the traditional differential privacy, the proposed solution guarantees enhanced privacy to meet the demands of customization. The experimental results show that our scheme is more refined in terms of user heterogeneity and the utility of data than others without violating privacy.

Private set intersection (PSI) is a fundamental cryptographic primitive, allowing two parties to calculate the intersection of their data sets without exposing additional private information. In cloud-based IoT system, IoT-enabled devices would like to outsource their data sets in their encrypted form to the cloud. In this scenario, how to delegate the set intersection computation over outsourced encrypted data sets to the cloud and how to achieve the fine-grained access control for PSI without divulging any additional information to the cloud are still open problems. With that in mind, in this work, we combine key-policy attribute-based encryption (KP-ABE) and PSI to introduce such a novel concept, called delegated key-policy attribute-based set intersection over outsourced encrypted data sets (KP-ABSI), to solve this problem. Then we propose a first concrete KP-ABSI scheme and analyze its efficiency.

Hydrological prediction in ungauged basins often relies on the parameter transplant method, which incurs high labor costs due to its dependence on expert input. To address these issues, we propose a novel hydrological prediction model named STH-Trans, which leverages multiple spatiotemporal views to enhance its predictive capabilities. Firstly, we utilize existing geographic and topographic indicators to identify and select watersheds that exhibit similarities. Subsequently, we establish an initial regression model using the TrAdaBoost algorithm based on the hydrologic data from the selected watershed stations. Finally, we refine the initial model by incorporating multiple spatiotemporal views, employing semi-supervised learning to create the STH-Trans model. The results of our experiments underscore the efficiency of the STH-Trans model in predicting runoff for ungauged basins. This innovation leads to a substantial increase in model accuracy ranging from 7.9% to 30% compared to various conventional methods. The model not only offers data support for water resource management, flood mitigation, and disaster relief efforts, but also provides decision support for hydrologists.

In a multimedia cloud computing system, suppose all cloud users outsource their own data sets to the cloud in the encrypted form. Each outsourced set is associated with an access structure such that a valid data user, Bob, with the credentials satisfying the access structure is able to conduct computing over outsourced encrypted set (e.g., decryption or other kinds of computing function). Suppose Bob needs to compute the set intersection over a data owner Alice’s and his own outsourced encrypted sets. Bob’s simple solution is to download Alice’s and Bob’s outsourced encrypted sets, perform set intersection operation, and decrypt the set intersection ciphertexts. A better solution is for Bob to delegate the cloud to calculate the set intersection, without giving the cloud any ability in breaching the secrecy of the sets. To solve this problem, this work introduces a novel primitive called ciphertext-policy attribute-based encryption with outsourced set intersection for multimedia cloud computing. It is the first cryptographic algorithm supporting a fully outsourced encrypted storage, computation delegation, fine-grained authorization security for ciphertext-policy model, without relying on an online trusted authority or data owners, and multi-elements set, simultaneously. We construct a scheme that provably satisfies the desirable security properties, and analyze its efficiency.

With the development of smart grid, smart meters play an important role during the process of data collection and delivery, where users’ electricity consumption information may be exposed to malicious users. The attacks to these sensitive information may touch off serious privacy concerns and pose a threat to the security of power system. In this paper, we design a novel framework to satisfy both personalized privacy preservation and customized system cost in the smart metering system, which contains a Rechargeable Battery (RB). First, we adopt differential privacy algorithm to enhance privacy and define the privacy preservation level for the privacy measure. Second, we specially design a comprehensive cost computing system, including electricity cost, battery operation cost, and the privacy budget. Then Markov Decision Process (MDP) is utilized in this work to formulate an optimization problem figuring out the trade-off between privacy and cost. Deploying Sarsa algorithm to address the optimization problem, we can finally obtain an optimal energy management system for different customers according to their personalized demands. Experimental evaluation results show that our system is able to balance privacy protection and cost saving with the introduction of RB.

The data security of fog computing is a key problem for the Internet of things. Identity-based encryption (IBE) from lattices is extremely suitable for fog computing. It is able to not only simplify certificate management, but also resist quantum attacks. In this paper, firstly, we construct a novel efficient lattice-based IBE scheme with Combined Public Key (CPK) technique by keeping from consumptive trapdoor generation algorithm and preimage sampling algorithm, which is required by the existing lattice-based IBE schemes based on learning with errors (LWE). In addition, its key storage cost is lower and it is IND-ID-CPA secure in the random oracle model. Furthermore, based on this, an enhanced lattice-based IBE scheme with IND-ID-CCA security is developed by employing strong one-time signature. Our schemes only need O(n³/log n) additions of vectors, while the existing schemes need at least O(n³) of additions and multiplications in Setup and Extract phase.