Smart health system with deep kronecker network-based key generation for privacy-aware aggregate authentication and access control in IoT

IF 1.1 4区 数学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
M. Sathya, V. Mareeswari, M. Jeyaselvi, A. Solairaj
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引用次数: 0

Abstract

The Internet of Things (IoT) application is an application and service that incorporates both the physical and information world. Similarly, it is difficult for existing health systems to provide privacy-aware aggregate authentication and fine-grained access control. To bridge the concern, a smart health system (SHS) with Deep Kronecker Network_key generation (DKN_keyGen) for privacy-aware aggregate authentication and access control in IoT is implemented. Here, entities employed for this model such as data owner (DO), registration center (RC), data user (DU) and cloud service provider (CSP). The method follows four steps, such as system initialization, user registration, Health data outsourcing and Health data access. Initially, the RC needs to initialize the security parameters, random parameters and public keys. After that, DO and DU must be registered in RC. Moreover, the smart health care data of DO generates the secret parameter and also obtains the secret parameter from the RC. The cloud storage stores and manages health care data in the health data outsourcing step. Finally, for health data access, the user gives appropriate parameters and access to the data which is implemented in the data access phase. The model is established considering different security functionalities including Encryption, ECC, XoR and hashing function. Here, the key is generated using DKN. The proposed model obtained a minimum computation time of 6.857 s, memory usage of 30 MB, and communication cost of 20.

基于深度kronecker网络的智能健康系统,用于物联网中隐私感知聚合认证和访问控制
物联网(IoT)应用是一种融合了物理世界和信息世界的应用和服务。同样,现有的医疗系统也很难提供隐私感知的聚合身份验证和细粒度访问控制。为了解决这一问题,实现了一个具有深度Kronecker网络密钥生成(DKN_keyGen)的智能健康系统(SHS),用于物联网中的隐私感知聚合身份验证和访问控制。这里,用于此模型的实体,如数据所有者(DO)、注册中心(RC)、数据用户(DU)和云服务提供商(CSP)。该方法分为系统初始化、用户注册、Health数据外包和Health数据访问四个步骤。首先,RC需要初始化安全参数、随机参数和公钥。之后,DO和DU必须在RC注册。此外,DO的智能医疗数据生成秘密参数,并从RC获取秘密参数。云存储在健康数据外包步骤中存储和管理医疗保健数据。最后,对于健康数据访问,用户提供适当的参数和对数据的访问,这在数据访问阶段实现。该模型考虑了不同的安全功能,包括加密、ECC、异或和哈希功能。这里,密钥是使用DKN生成的。该模型的最小计算时间为6.857 s,内存占用为30 MB,通信开销为20。
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来源期刊
Journal of Combinatorial Optimization
Journal of Combinatorial Optimization 数学-计算机:跨学科应用
CiteScore
2.00
自引率
10.00%
发文量
83
审稿时长
6 months
期刊介绍: The objective of Journal of Combinatorial Optimization is to advance and promote the theory and applications of combinatorial optimization, which is an area of research at the intersection of applied mathematics, computer science, and operations research and which overlaps with many other areas such as computation complexity, computational biology, VLSI design, communication networks, and management science. It includes complexity analysis and algorithm design for combinatorial optimization problems, numerical experiments and problem discovery with applications in science and engineering. The Journal of Combinatorial Optimization publishes refereed papers dealing with all theoretical, computational and applied aspects of combinatorial optimization. It also publishes reviews of appropriate books and special issues of journals.
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