A Multi-grained Log Auditing Scheme for Cloud Data Confidentiality

Yang, Zhen; Wang, Wenyu; Huang, Yudong; Li, Xing

doi:10.1007/s11036-019-01328-1

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…Compliance with relevant laws and regulations is essential for mental health organizations to protect patient confidentiality and avoid legal repercussions ( Gooding & Kariotis, 2021 ). Audit logs and monitoring play a crucial role in detecting and responding to potential security breaches ( Yang et al, 2021 ). By monitoring access logs and system activities, organizations can identify suspicious behavior and take prompt action ( Ali, Ahmed & Khan, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

An adaptive data-driven architecture for mental health care applications

Sundaram,

Subramaniam,

Ab Hamid

et al. 2024

PeerJ

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Background In the current era of rapid technological innovation, our lives are becoming more closely intertwined with digital systems. Consequently, every human action generates a valuable repository of digital data. In this context, data-driven architectures are pivotal for organizing, manipulating, and presenting data to facilitate positive computing through ensemble machine learning models. Moreover, the COVID-19 pandemic underscored a substantial need for a flexible mental health care architecture. This architecture, inclusive of machine learning predictive models, has the potential to benefit a larger population by identifying individuals at a heightened risk of developing various mental disorders. Objective Therefore, this research aims to create a flexible mental health care architecture that leverages data-driven methodologies and ensemble machine learning models. The objective is to proficiently structure, process, and present data for positive computing. The adaptive data-driven architecture facilitates customized interventions for diverse mental disorders, fostering positive computing. Consequently, improved mental health care outcomes and enhanced accessibility for individuals with varied mental health conditions are anticipated. Method Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, the researchers conducted a systematic literature review in databases indexed in Web of Science to identify the existing strengths and limitations of software architecture relevant to our adaptive design. The systematic review was registered in PROSPERO (CRD42023444661). Additionally, a mapping process was employed to derive essential paradigms serving as the foundation for the research architectural design. To validate the architecture based on its features, professional experts utilized a Likert scale. Results Through the review, the authors identified six fundamental paradigms crucial for designing architecture. Leveraging these paradigms, the authors crafted an adaptive data-driven architecture, subsequently validated by professional experts. The validation resulted in a mean score exceeding four for each evaluated feature, confirming the architecture’s effectiveness. To further assess the architecture’s practical application, a prototype architecture for predicting pandemic anxiety was developed.

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Section: Discussionmentioning

confidence: 99%

An adaptive data-driven architecture for mental health care applications

Sundaram,

Subramaniam,

Ab Hamid

et al. 2024

PeerJ

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“…Mao et al [ 13 ] proposed a position-aware Merkle tree that can compute the root node without retrieving the whole Merkle tree; in this way, the storage complexity, computation cost, and communication cost will be reduced. Yang et al [ 14 ] propose a multi-grained log auditing scheme to improve the efficiency, which introduces a Merkle Hash Tree structure to support multiple data granularity in logs. Based on the multi-grained log, they present a multi-grained data confidentiality auditing.…”

Section: Related Workmentioning

confidence: 99%

A Security Management and Control Solution of Smart Park Based on Sensor Networks

Zhao¹,

Tian²,

Niu³

et al. 2021

Sensors

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As a typical application of sensor networks, there exist many information security problems in smart parks, such as confusion of personnel access, lack of security management, disorderly data flow, insufficient collection of audit evidence, and so on. Aiming at the scenario of personnel and equipment moving in different areas of smart parks, the paper proposes a joint authorization and dynamic access control mechanism, which can provide unified identity management services, access control services, and policy management services, and effectively solve the problem of multi-authorization in user identity and authority management. The license negotiation interaction protocol is designed to prevent common network attack threats in the process of identity authentication and authority management. In order to realize the tamper-proof storage of personnel and equipment movement trajectory, the paper also designs a movement trajectory traceability protocol based on a Merkle tree, which solves the problems of internal personnel malicious attack, trusted third-party dependency bottleneck, high overheads of tracking algorithms, and so on. The experimental results show that compared with the current security control mechanisms for sensor networks, the joint authorization, and dynamic access control mechanism can support multi-party authorization and traceability, while the overhead it generates in initialization, encryption, decryption, and key generation steps are basically the same as other mechanisms do.

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