In neuroscience, phase synchronization (PS) is a crucial mechanism that facilitates information processing and transmission between different brain regions. Specifically, global phase synchronization (GPS) characterizes the degree of PS among multivariate neural signals. In recent years, several GPS methods have been proposed. However, they primarily focus on the collective synchronization behavior of multivariate neural signals, while neglecting the structural difference between oscillator networks. Therefore, in this paper, we introduce a method named total correlation-based synchronization (TCS) to quantify GPS intensity by examining network organization. To evaluate the performance of TCS, we conducted simulations using the Rössler model and compared it to three existing methods: circular omega complexity, hyper-torus synchrony, and symbolic phase difference and permutation entropy. The results indicate that TCS outperforms the other methods at distinguishing the GPS intensity between networks with similar structures. And it offers insight into the separation and integration behavior of signals during synchronization. Furthermore, to validate this method with experimental data, TCS was applied to analyze the GPS variation of multichannel stereo-electroencephalography (SEEG) signals recorded from onset zones of patients with temporal lobe epilepsy. It was observed that the termination of seizures was associated with the increased GPS and the integration of brain regions. Taken together, TCS offers an alternative way to measure GPS of multivariate signals, which may shed new lights on the mechanism of brain functions and neurological disorders, such as learning, memory, epilepsy, and Alzheimer's disease.

The private intersection-sum with cardinality (PIS-CA) protocol enables two parties to privately compute the cardinality of the intersection between their datasets and the sum of the values associated with these intersecting elements, while keeping all other information confidential. As a related variant of private set intersection (PSI), private set intersection with cardinality (PSI-CA) protocols compute only the intersection size without considering the associated values. Existing PIS-CA protocols attempt to reduce the amount of communication through batch encryption-based optimization, but their incomplete design and implementation hinder their practical deployment. Moreover, the shuffle proof withO(√n) communication complexity adopted in their protocol incur substantial communication overhead, further limiting their scalability. To address these problems, we propose an optimized two-sided PIS-CA protocol in the malicious model. Our scheme provides a concrete and implementable batch encryption design that achieves practical communication efficiency, together with an enhanced lightweight shuffle proof based on the Curdleproofs framework. The experimental results demonstrate that the proposed protocol significantly reduces the total communication cost, making it suitable for privacy-preserving applications such as ad conversion measurement.

Summary Many schemes for privacy‐preserving machine learning have been proposed over the past decade. Often, the entities want to keep the privacy of their data while performing machine learning tasks collaboratively, and institutions or end‐users are with limited computing and storage resources. To overcome these issues and to take benefits of cloud computing, it is possible to outsource the execution of a machine learning task to a computing service while retaining confidentiality of the participant's data. Clustering is one of the commonly used tasks in various machine learning and data mining applications. In this paper, we demonstrate that, by using homomorphic encryption, it is possible to outsource the execution of a two‐party k‐means clustering algorithm to a single cloud server while retaining confidentiality of the test data. To the best of our knowledge, ours is the first reasonable scheme to discuss the two‐party k‐means clustering algorithm to a single cloud server.

Computations over biometric data performed on untrusted cloud environment raises important concerns about the privacy of biometrics data. Face recognition has been widely applied in a variety of enterprise, civilian and law enforcement. Many schemes for privacy-preserving face recognition (PPFR) have been investigated over the past decade. In order to protect individuals' privacy, face recognition is performed over encrypted face images. However, these results increase the computation cost of the client and the face database owners with limited computing and storage resources. To overcome this kind of issue and to take benefits of cloud computing, outsourcing such tasks to the cloud environment has recently gained special attention. Currently, no secure techniques for outsourcing face biometric recognition are readily available to make client and the face database owners free from encryption and decryption operations. We consider the scenario where a client and a database owner of face images securely outsource their data to the cloud and ask the cloud to perform the face recognition task on their combined data in a privacy-preserving manner. We term such a process as privacy-preserving and outsourced face recognition (PPOFR). We propose a novel and efficient scheme to the PPOFR problem with outsourced computation for the first time under a federated cloud environment based on the Eigenfaces algorithm, which efficiently protects data confidentiality of the participating entities under the standard semi-honest model. To the best of our knowledge, ours is the first work to discuss and propose a comprehensive solution to the PPOFR problem that incurs negligible cost on the participating entities. We theoretically estimate both the computation and communication costs of the proposed protocol.