Jinjun Chen’s research while affiliated with Swinburne University of Technology and other places

Notary cross-chain transaction technologies have obtained broad affirmation from industry and academia as they can avoid data islands and enhance chain interoperability. However, the increased privacy concern in data sharing makes the participants hesitate to upload sensitive information without the trust foundation of the external network. To address this issue, this paper proposes a differential private notary mechanism (DPNM) to preserve privacy in blockchain interoperations. It establishes a fully trusted notary organization to conduct data perturbation before replying query to the external blockchain network. In addition, the DPNM contains two built-in privacy budget allocation schemes: Efficiency priority scheme (EPS) and Privacy priority scheme (PPS). These schemes unify the privacy preferences among different nodes based on multi-node consensus in the decentralized environment. The EPS can generate noise linearly and work efficiently, and the PPS reflects better on nodes’ preferences. This paper utilizes several metrics including mechanism errors, elapsed time, latency, and gas consumption to evaluate the performance of DPNM compared to the traditional mechanisms. The experiment results indicate that the proposed mechanism can meet privacy preferences among different nodes and provide better utility with little extra cost.

Differential private (DP) query and response mechanisms have been widely adopted in various applications based on Internet of Things (IoT) to leverage variety of benefits through data analysis. The protection of sensitive information is achieved through the addition of noise into the query response which hides the individual records in a dataset. However, the noise addition negatively impacts the accuracy which gives rise to privacy-utility trade-off. Moreover, the DP budget or cost is often fixed and it accumulates due to the sequential composition which limits the number of queries. Therefore, in this paper, we propose a framework known as optimized privacy-utility trade-off framework for data sharing in IoT (OPU-TF-IoT). Firstly, OPU-TF-IoT uses an adaptive approach to utilize the DP budget by considering a new metric of population or dataset size along with the query. Secondly, our proposed heuristic search algorithm reduces the DP budget accordingly whereas satisfying both data owner and data user. Thirdly, to make the utilization of DP budget transparent to the data owners, a blockchain-based verification mechanism is also proposed. Finally, the proposed framework is evaluated using real-world datasets and compared with the traditional DP model and other related state-of-the-art works. The results demonstrate that our proposed framework not only utilizes the DP budget efficiently, but also optimizes the number of queries by 49% and 54% on average compared to state-of-the-art and standard DP models, respectively. Furthermore, the data owners can effectively make sure that their data is shared accordingly through our blockchain-based verification mechanism which encourages them to share their data into the IoT system.

The post-processed Differential Privacy (DP) framework has been routinely adopted to preserve privacy while maintaining important invariant characteristics of datasets in data-release applications such as census data. Typical invariant characteristics include non-negative counts and total population. Subspace DP has been proposed to preserve total population while guaranteeing DP for sub-populations. Non-negativity post-processing has been identified to inherently incur fairness issues. In this work, we study privacy and unfairness ( i.e ., accuracy disparity) concerns in the post-processed DP framework. On one hand, we propose the post-processed DP framework with both non-negativity and accurate total population as constraints would inadvertently violate privacy guarantee desired by it. Instead, we propose the post-processed subspace DP framework to accurately define privacy guarantees against adversaries. On the other hand, we identify unfairness level is dependent on privacy budget, count sizes as well as their imbalance level via empirical analysis. Particularly concerning is severe unfairness in the setting of strict privacy budgets. We further trace unfairness back to uniform privacy budget setting over different population subgroups . To address this, we propose a varying privacy budget setting method and develop optimization approaches using ternary search and golden ratio search to identify optimal privacy budget ranges that minimize unfairness while maintaining privacy guarantees. Our extensive theoretical and empirical analysis demonstrates the effectiveness of our approaches in addressing severe unfairness issues across different privacy settings and several canonical privacy mechanisms. Using datasets of Australian Census data, Adult dataset, and delinquent children by county and household head education level, we validate both our privacy analysis framework and fairness optimization methods, showing significant reduction in accuracy disparities while maintaining strong privacy guarantees.

Advanced mobile communication and data processing technologies have promoted the development of Internet of Things (IoT), but they have also posed challenges to the distributed federated learning mode in the field of Internet of Vehicles (IoV). Faced with a large number of vehicle nodes available for federated training in IoV, the federated learning training task becomes challenging when motivating a large number of vehicles participant. A difficulty posed for federated learning in IoV is heterogeneity challenges caused by massive device participation. Moreover, excessive resource and system maintenance costs associated with a large number of poor-quality devices participating in federated training cannot be ignored. To address these issues, this paper proposes a novel vehicle device selection and aggregation joint optimization model based on a many-objective evolutionary algorithm. The proposed model can be optimized by BiGE algorithm to obtain an optimal subset of vehicle equipment and corresponding weight assignment scheme, thus reducing unnecessary resources waste and budget expenditure while ensuring global model performance. To verify the feasibility of the model, several sets of experiments are conducted to demonstrate that our proposed model has acceptable performance while largely reducing the number budget of participants.