Metric Differential Privacy (mDP) extends the local differential privacy (LDP) framework to metric spaces, enabling more nuanced privacy protection for data such as geo-locations.However, existing mDP optimization methods, particularly those based on linear programming (LP), face scalability challenges due to the quadratic growth in decision variables.In this paper, we propose Perturbation via Anchor-based Distributed Approximation (PAnDA), a scalable two-phase framework for optimizing metric differential privacy (mDP).To reduce computational overhead, PAnDA allows each user to select a small set of anchor records, enabling the server to solve a compact linear program over a reduced domain.We introduce three anchor selection strategies, exponential decay (PAnDA-e), power-law decay (PAnDA-p), and logistic decay (PAnDA-l), and establish theoretical guarantees under a relaxed privacy notion called probabilistic mDP (PmDP).Experiments on real-world geo-location datasets demonstrate that PAnDA scales to secret domains with up to 5,000 records, two times larger than prior LP-based methods, while providing theoretical guarantees for both privacy and utility.

Financial statement analysis is crucial to organizational performance, but quantitative and rule-based methods often fail to capture nonlinear relationships, uncertainty, and rapidly changing market conditions. A hybrid ANFIS-QIGA-DFRL framework that integrates Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for interpretable reasoning, Quantum-Inspired Genetic Algorithms (QIGA) for efficient optimization of fuzzy rules and membership functions, and Deep Fuzzy Reinforcement Learning (DFRL) for continuous adaptive policy learning from real-time data is proposed to address these challenges On the CSMAR dataset of Chinese firms, the framework improves convergence time by 40%, mean squared error by 0.017, and prediction accuracy by 91.2%. Compared to ANFIS and ReNN-PSO, it has a policy stability score of 0.86 and decreases regret by over 70%. These findings demonstrate the model’s improved financial risk forecasting and decision support scalability, interpretability, and adaptability. The proposed approach uniquely balances optimization efficiency with dynamic learning, ensuring faster convergence and more reliability under uncertain financial situations than hybrid fuzzy and reinforcement learning systems. This study introduces a scalable, self-improving, and interpretable automated financial analysis tool for real-time health assessment, risk management, and investment strategy support in emerging markets.

Geo-obfuscation serves as a location privacy protection mechanism (LPPM), enabling mobile users to share obfuscated locations with servers, rather than their exact locations. This method can protect users’ location privacy when data breaches occur on the server side since the obfuscation process is irreversible. To reduce the utility loss caused by data obfuscation, linear programming (LP) is widely employed, which, however, might suffer from a polynomial explosion of decision variables, rendering it impractical in largescale geo-obfuscation applications. In this paper, we propose a new LPPM, called Locally Relevant Geo-obfuscation (LR-Geo), to optimize geo-obfuscation using LP in a time-efficient manner. This is achieved by confining the geoobfuscation calculation for each user exclusively to the locally relevant (LR) locations to the user’s actual location. Given the potential risk of LR locations disclosing a user’s actual whereabouts, we enable users to compute the LP coefficients locally and upload them only to the server, rather than the LR locations. The server then solves the LP problem based on the received coefficients. Furthermore, we refine the LP framework by incorporating an exponential obfuscation mechanism to guarantee the indistinguishability of obfuscation distribution across multiple users. Based on the constraint structure of the LP formulation, we apply Benders’ decomposition to further enhance computational efficiency. Our theoretical analysis confirms that, despite the geo-obfuscation being calculated independently for each user, it still meets geo-indistinguishability constraints across multiple users with high probability. Finally, the experimental results based on a real-world dataset demonstrate that LR-Geo outperforms existing geo-obfuscation methods in computational time, data utility, and privacy preservation.

High-resolution scene classification is a fundamental yet challenging problem due to rich image variations in viewpoint, object pose and spatial resolution, etc, which results in large within-class diversity and high between-class similarity. In the paper we focus on tackling the problem of how to learn appropriate feature representation for high-resolution scene classification. To achieve better scene representation, we proposed a combined CNN feature learning framework in multi-scale multi-layer based Gaussian coding (mSmL-Gcoding) manner. In addition, a novel feature coding with Gaussian descriptor is introduced to enhance the discriminative ability of CNN features. Experimental results on two publicly available challenging scene datasets validated that the effectiveness of our method and found it compared favorably with state-of-the-arts.