For digital health platforms, the challenge is balancing patient privacy with the ability to match patients to the right providers quickly and accurately. Existing systems often suffer from privacy leakage, insufficient matching precision, and degraded performance when dealing with large-scale data. In this paper, we propose 4PM, a novel privacy-preserving patient-provider matching scheme that leverages secure computation to deliver strong privacy guarantees while ensuring efficient and accurate matching. Our method partitions patient data between two non-colluding servers via secret sharing, employing the optimized Millionaires' Protocol for secure ranking and leveraging oblivious retrieval techniques for privacy-preserving matching. 4PM significantly reduces the computational complexity of high-dimensional data, achieving end-to-end latency within 0.5 seconds in scenarios with 200 doctors and 200-dimensional symptom vectors. Our work contributes to fostering secure and trustworthy healthcare in the digital era.

Federated learning (FL) is a pivotal paradigm for decentralized model training while preserving data privacy. However, data heterogeneity among clients significantly degrades model performance and convergence efficiency. In response, we introduce a federated knowledge distillation mechanism, FedEXD, that addresses robustness and convergence in diverse client environments through a self-propelled learning architecture. FedEXD employs a novel density ratio-based data extraction algorithm, leveraging KLIEP to select representative data, enhancing global knowledge synthesis and local model adaptability while preserving privacy. Extensive evaluations on benchmark datasets demonstrate FedEXD's substantial improvements in efficiency and accuracy, demonstrating a substantial 1.51% accuracy improvement over state-of-the-art methods under firm heterogeneity while reducing communication rounds by over 46.3%. These findings underscore FedEXD's potential to advance FL systems' generalizability across complex, non-IID data distributions, offering a scalable solution for privacy-conscious, high-performance distributed learning.

Smart meter-based individual load forecasts are more and more widely deployed to serve smart grid and home energy management. Customary load forecasting systems collect a massive amount of fine-grained electrical data from people’s smart meters in plaintext, inevitably raising privacy concerns and even anti-smart-meter initiatives. Current privacy solutions either compromise accuracy and efficacy or require the redeployment of trusted infrastructure. In this paper, we present PrivGrid, the first systematic solution for smart grids that collects, clusters, trains, and forecasts customers’ load data in a privacy-preserving way. Moreover, we highlight the technical contribution of our building block: a novel and fast arithmetic multiplication triple via secure inner product protocol outperforms the existing methods and may be included in other privacy computing modules. Then, we develop efficient secure protocols to enable the arithmetic operations of individual load forecasting in a server-aided model and utilize the best alternatives to nonlinear functions. Besides, aggregating all of our individual forecasts can produce a more accurate estimate of the system-level load than the typical aggregate technique. We rigorously prove that the servers cannot obtain the user’s historical load data and short-term load forecast values while providing services. PrivGrid is also tested on real residential smart meter data to show its efficiency, and the relevant code has been made available to the community for further research.

Privacy concerns of using sensitive biometric data as credentials arise with the wide adoption of user-friendly face authentication. To protect the facial features of users, two important functions, i.e.,revocabilityandreusability, are anticipated to be realized in a privacy-preserving face authentication design. Revocability requires an effective approach to deregister or replace user credentials when the authentication server is compromised; For reusability, the same credentials should appear independently to non-cooperating applications. Accomplishing these two properties is challenging as the uniqueness of facial features. In this article, we presentPrivFace, a fast privacy-preserving face authentication, supporting revocable, and reusable biometric credentials. The core innovation is a novel secure inner product protocol that employs a lightweight random masking technique instead of time-consuming public-key cryptographic operations to efficiently measure facial data similarity. We rigorously analyze the security to show that the server cannot acquire the user's sensitive biological features during the authentication. Our experiment with real-world datasets shows thatPrivFaceis friendly to edge smart devices, which takes less than$100 ms$per successful authentication on a common smartphone and outperforms the prior art J. Lei, Q. Peiet al.[1]. by$20 \times$. We have made the relevant codes open-source to the community for further research.