The rapid growth of Electric Vehicles (EVs) has brought significant challenges in ensuring the privacy of sensitive data generated, particularly in Vehicle-to-Vehicle (V2V) energy trading systems. This study examines methods to balance data privacy preservation with the utility required for EV-related services. Existing privacy-preserving techniques often struggle to strike a balance between privacy and utility, particularly in dynamic environments where data sensitivity and usage patterns are constantly changing. In this paper, we propose an Adaptive Private Stable Matching (APSM) algorithm that incorporates a dynamic privacy budget algorithm for Differential Privacy (DP). APSM provides stable, privacy-preserving matches for EVs participating in V2V energy trading. The dynamic privacy budget mechanism adjusts allocation according to the number of EVs, offering enhanced privacy protection when necessary and increased utility when feasible. The proposed approach optimizes the utilization of the privacy budget, meeting both strict privacy requirements and ensuring efficient service delivery. Experimental results show that the technique outperforms static approaches in terms of privacy budget management, thereby enhancing privacy protection while maintaining high data utility. This combination renders APSM highly suitable for practical V2V energy trading scenarios, delivering robust privacy safeguards without compromising system performance.

Differential privacy (DP) is a leading paradigm for privacy preservation in statistical analysis and learning. Traditional DP mechanisms add noise independently of the original data, which yields inconsistent perturbations across groups and raises fairness concerns in downstream decision and learning tasks. Prior work often assesses fairness via bias and variance, while overlooking noise direction and the scale of the underlying query. We propose a novel Fairly Proportional Noise Mechanism (FPNM) that uniquely considers both the direction and magnitude of noise relative to raw query results. We define mathematical formulations for unfairness, factoring in weighting and temporal decay to allow nonlinear amplification of unfairness. The privacy analysis shows a negative correlation between unfairness and privacy strength that higher privacy levels lead to increased noise and thus greater unfairness. We then generalize to group-level assessment using the average unfairness and the Frobenius norm ($F$-Norm). We also prove that adaptive budget reallocation within a data independent feasible domain preserves the overall$(\epsilon ,\delta )$-DP guarantee. Experiments on both decision and learning tasks demonstrate consistent gains. In decision tasks, the proposed FPNM effectively reduces unfairness, achieving average reductions of 19.17% and 17.32% in$F$-Norm and average unfairness, respectively. In learning tasks, integrating FPNM with DP-SGD achieves fairness comparable to fairness-aware baselines and better accuracy. Besides, empirical privacy remains intact under membership inference attacks. These results highlight its effectiveness in improving utility while preserving privacy, offering a robust and comprehensive approach to enhancing fairness in DP.

Accurately detecting out-of-scope queries is a challenging task in task-oriented dialog systems. Most existing research focus on adding an outlier detector after classification or designing an open world classification to identify unknown intents. There is still a major performance gap on achieving high efficiency and accuracy based on above methods. In our research, we tend to solve this problem by constructing an out-of-scope class in the classification. We propose an explainable centroid-based fine-tuning method including a modified decision metric (MDM) and a centroid-based cosine loss (CCL) on Pre-trained Transformer models for optimization. This loss function builds on Copernican structure and assigns the same margin to each in-scope class to resolve an ambiguous configuration on out-of-scope detection. Moreover, cosine similarity is utilized to remove radial variations of centroids. Experimental results show that our proposed method achieves improvement compared to other baseline methods.

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 Privacy (DP) is a preeminent technique for data privacy by introducing noise to sensitive information. However, traditional DP mechanisms excessively rely on third parties to ensure traceability, necessitating strong background assumptions that are frequently impractical in real-world scenarios. This reliance makes it difficult to preserve both privacy and traceability. To address these challenges, we propose a novel Traceable and Collision-Resilient Differential Privacy (TCRDP) mechanism. The TCRDP mechanism simultaneously publishes perturbed results and data fingerprints, retaining partial information from the original data in a collision-resilient manner to facilitate future verification. Moreover, the TCRDP mechanism integrates an innovative noise generation process, leveraging hash values and a customized Laplace-like distribution to produce noise. This strategy mitigates the risk of adversaries compromising privacy through enumeration and yields a more concentrated noise distribution with reduced variance. We evaluated the TCRDP mechanism using three datasets: ICUs, Diabetes, and RAHRD, across various query types. The experimental results demonstrated significant improvements in data utility, with the TCRDP mechanism achieving great reductions in Mean Absolute Error (MAE) and Mean Squared Error (MSE) compared to traditional mechanisms. The TCRDP mechanism also maintained lower Accuracy Loss (AL) across different privacy budgets and dataset sizes, highlighting its robustness and scalability. These findings underscore the potential of the TCRDP mechanism to advance privacy-preserving data analysis, offering significant enhancements over existing methods in both accuracy and utility.

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 thepost-processed subspace DP frameworkto 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 touniform privacy budget setting over different population subgroups. To address this, we propose avarying privacy budget settingmethod 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.

The objective of differential privacy (DP) is to protect privacy by producing an output distribution that is indistinguishable between any two neighboring databases. However, traditional differentially private mechanisms tend to produce unbounded outputs in order to achieve maximum disturbance range, which is not always in line with real-world applications. Existing solutions attempt to address this issue by employing post-processing or truncation techniques to restrict the output results, but at the cost of introducing bias issues. In this paper, we propose a novel differentially private mechanism which uses a composite probability density function to generate bounded and unbiased outputs for any numerical input data. The composition consists of an activation function and a base function, providing users with the flexibility to define the functions according to the DP constraints. We also develop an optimization algorithm that enables the iterative search for the optimal hyper-parameter setting without the need for repeated experiments, which prevents additional privacy overhead. Furthermore, we evaluate the utility of the proposed mechanism by assessing the variance of the composite probability density function and introducing two alternative metrics that are simpler to compute than variance estimation. Our extensive evaluation on three benchmark datasets demonstrates consistent and significant improvement over the traditional Laplace and Gaussian mechanisms. The proposed bounded and unbiased composite differentially private mechanism will underpin the broader DP arsenal and foster future privacy-preserving studies.

Blockchain has gradually attracted widespread attention from the research community of the IoT, due to its decentralization, consistency, and other attributes. It builds a secure and robust system by generating a backup locally for each participant node to collectively maintain the network. However, this feature brings some privacy concerns since all nodes can access the chain data, users’ sensitive information under risk of leakage. The local differential privacy (LDP) mechanism can be a promising way to address this issue as it implements data perturbation before uploading to the chain. While traditional LDP mechanisms cannot fit well with the blockchain since the requirements of a fixed input range, large data volume, and using the same privacy budget, which are practically difficult in a decentralized environment. To overcome these problems, we propose a novel LDP mechanism to split input numerical data and implement perturbation by digital bits, which does not require a fixed input range and large data volume. In addition, we use an iteration approach to adaptively allocate the privacy budget for different perturbation procedures that minimize the total deviation of perturbed data and increase the data utility. We employ mean estimation as the statistical utility metric under the same and randomized privacy budgets to evaluate the performance of our novel LDP mechanism. The experiment results indicate that the proposed LDP mechanism performs better in different scenarios, and our adaptive privacy budget allocation model can significantly reduce the deviation of the perturbation function to provide high data utility while maintaining privacy.