Fine-Grained Access Control for Personal Health Records in Cloud Computing

Since cloud computing and other data outsourcing settings offer high flexibility and accessibility, many healthcare providers se personal health records (PHRs) that are electronic to let patients manage their own health data in a scalable and robust environment. However, PHRs contain extremely sensitive data, thus security and privacy issues are crucial. PHR owners should be free to create their own flexible and safe access rules for their data that is outsourced. Beyond the fundamental authentication feature, most commercial cloud platforms offer tenants the option of symmetric or public key encryption to protect their data. But because symmetric encryption has a large key management overhead and requires a lot of upkeep, In this study, we develop a fine-grained, secure access control system for outsourced PHRs that incorporates updates to the policies in a lightweight manner. Our suggested solution (PRE) is based on proxy re-encryption and ciphertext policy attribute-based encryption (CP-ABE). To assist complete policy change tracing, we also offer a policy versioning approach. Ultimately, an assessment of performance was carried out to demonstrate the efficacy of the suggested approach.

Personal health record (PHR) is an emerging patient-centric model of health information exchange, which is often outsourced to be stored at a third party, such as cloud providers. However, there have been wide privacy concerns as personal health information could be exposed to those third party servers and to unauthorized parties. To assure the patients' control over access to their own PHRs, it is a promising method to encrypt the PHRs before outsourcing. Yet, issues such as risks of privacy exposure, scalability in key management, flexible access, and efficient user revocation, have remained the most important challenges toward achieving fine-grained, cryptographically enforced data access control. In this paper, we propose a novel patient-centric framework and a suite of mechanisms for data access control to PHRs stored in semitrusted servers. To achieve fine-grained and scalable data access control for PHRs, we leverage attribute-based encryption (ABE) techniques to encrypt each patient's PHR file. Different from previous works in secure data outsourcing, we focus on the multiple data owner scenario, and divide the users in the PHR system into multiple security domains that greatly reduces the key management complexity for owners and users. A high degree of patient privacy is guaranteed simultaneously by exploiting multiauthority ABE. Our scheme also enables dynamic modification of access policies or file attributes, supports efficient on-demand user/attribute revocation and break-glass access under emergency scenarios. Extensive analytical and experimental results are presented which show the security, scalability, and efficiency of our proposed scheme.

Cloud computing and big data: Technologies and applications

A personal health record (PHR) system is a smart health system that serves patients and doctors. A PHR is usually stored in a cloud and managed by a semitrusted cloud provider. However, there is still a possibility of the exposure of personal health information to semitrusted parties and unauthorized users. To protect the privacy of patients and ensure that patients can control their PHRs, a patient-centric PHR sharing framework is proposed in this article. In this framework, all PHRs are protected with multiauthority attribute-based encryption before outsourcing, which solves the key hosting problem and achieves fine-grained access control to PHRs. Furthermore, an anonymous authentication between the cloud and the user is proposed to ensure data integrity on the cloud while not exposing the user's identity during authentication. The proposed authentication is issued from a new online-offline attribute-based signature. It can make the encrypted PHRs resist collusion attacks and not be forged during the period of sharing, which enhances patients' control of their PHRs. Online-offline and outsourcing decryption also reduces calculation costs and improves operational efficiency. Finally, comparisons are given based on numerical experiments.

Attribute-based encryption is a popular cryptographic technology to protect the privacy of clients' data in cloud computing. In order to make the scheme have the functionality of comparing ciphertext, Zhu et al. combined concepts of key-policy attributed-based encryption with public key encryption with equality test and proposed key-policy attributed-based encryption with equality test. They defined its security model and put forward a scheme from bilinear pairing. In this paper, we first use two methods to show that their scheme is not secure for one-way under chosen ciphertext attack. Next, we show that the scheme is not secure for a test under chosen ciphertext attack defined in their paper yet. Finally, we point out the definition of a test under chosen ciphertext attack is very strong, which causes no scheme to satisfy the model.

The sharing of personal health records can help to improve the accuracy of the doctor's diagnosis and to promote the progress of medical research. Currently, to reduce the maintenance cost of data, personal health records are usually outsourced to a third party such as the cloud service provider. In this case, patients may lose direct control over their personal health records and the semi-trusted cloud service provider may tamper with or reveal personal health records. Therefore, ensuring the privacy and integrity of personal health records and realizing the fine-grained access control are crucial issues when personal health records are shared. As a distributed architecture with decentralized and tamper-proof features, blockchain provides a new way to protect the personal health records sharing system. In this paper, we propose a new personal health records sharing scheme with data integrity verifiable based on blockchain. Aiming at the problems of privacy disclosure, limited keyword search ability and loss of control rights in the process of personal health record sharing, the new scheme uses searchable symmetric encryption and attribute-based encryption techniques to achieve privacy protection, keyword search, and fine-grained access control. Compared with the existing similar schemes, the new scheme allows patients to distribute attribute private key for users, avoiding many security problems caused by the existing of attribute authority in the scheme. Furthermore, the new scheme uses blockchain to manage keys in the scheme, avoiding the single point failure problem of centralized key management. In particular, the new scheme stores the hash values of encrypted personal health records in blockchain, and the related index set is stored in smart contract, which can further improve the efficiency of data integrity verification. Finally, performance evaluation and security analysis indicate that our scheme is secure and feasible for practical use.

In the quest for sustainable urban development, smart cities are increasingly harnessing cloud computing and big data analytics to optimize energy usage and drive policy initiatives. This review explores how leveraging these technologies can transform energy management within urban environments. Cloud computing provides the scalable infrastructure necessary for processing and storing vast amounts of data generated by smart sensors and IoT devices in real-time. By utilizing cloud-based platforms, cities can ensure the efficient aggregation, analysis, and dissemination of energy data, facilitating informed decision-making and policy formulation. Big data analytics plays a crucial role in this framework by enabling the analysis of complex and voluminous datasets to uncover patterns and insights that traditional methods may miss. Through advanced analytics, cities can gain a deeper understanding of energy consumption trends, predict peak demand periods, and identify opportunities for efficiency improvements. This data-driven approach allows policymakers to design and implement targeted energy policies, optimize resource allocation, and reduce operational costs. Key benefits of integrating cloud computing and big data analytics in energy optimization include enhanced real-time monitoring capabilities, improved accuracy in demand forecasting, and more effective management of energy resources. The ability to analyze and visualize large datasets supports the development of dynamic policies that adapt to changing conditions and promote energy efficiency across various sectors. Despite these advantages, challenges such as data privacy concerns, the need for robust cybersecurity measures, and the requirement for skilled personnel to interpret complex data must be addressed. Additionally, ensuring equitable access to these technologies across diverse urban areas is essential for maximizing their impact. In conclusion, the strategic application of cloud computing and big data analytics offers significant potential for optimizing energy use in smart cities. By embracing these technologies, urban centers can drive more effective and responsive energy policies, contributing to broader sustainability goals and enhancing overall quality of life.

Cloud Computing and Big Data continue to be disruptive forces in computing and have made inroads in the Computer Science curriculum, with courses in Cloud Computing and Big Data being routinely offered at the graduate and undergraduate level. One major challenge in offering courses in Big Data and Cloud Computing is resources. The question is how to provide students with authentic experiences making use of current Cloud and Big Data resources and tools and do so in a cost effective manner. Historically, three options, namely physical clusters, virtual clusters and cloud-based clusters, have been used to support Big Data and Cloud Computing courses. Virtual clusters and cloud-based options are those that institutions have typically adopted and many arguments in favor of these options exist in the literature, citing cost and performance. Here we argue that teaching Big Data and Cloud Computing courses can be done making use of a physical cluster and that many of the existing arguments fail to take into account many important factors in their calculations. These factors include the flexibility and control of a physical cluster in responding to changes in industry, the ability to work with much larger datasets, and the synergy and broad applicability of an appropriately equipped physical cluster for courses such as Cloud Computing, Big Data and Data Mining. We present three possible configurations of a physical cluster which span the spectrum in terms of cost and provide cost comparisons of these configurations against virtual and cloud-based options, taking into account the unique requirements of an academic setting. While limitations do exist with a physical cluster and it is not an option for all situations, our analysis and experience indicates that there is great value in using a physical cluster to support teaching Cloud Computing and Big Data courses and it should not be dismissed.

Cloud computing is an on-demand resource sharing paradigm that serves users through Internet. The resource sharing extends from data to computing infrastructure resources. The term “cloud computing” generally refers to data centres with sufficient infrastructure accessed by various users over the Internet. Besides the benefits of cloud computing, data security is a significant concern and it is highly vulnerable to multiple attacks. Different applications and domains utilize cloud computing services to store their sensitive data in cloud servers. The specific data that were targeted by the attackers are the banking account information, organization information, etc. The sensitive information in cloud computing encompasses a wide range of domains, including personal and official information about an organisation, which, when breached, causes the entire system to fail. Numerous data security attacks occur worldwide. A few of the attacks that contributed to the system’s collapse is Yahoo’s, which has the dubious distinction of having experienced the largest major data breach in attack history, compromising over 3 million compromised accounts. This attracts the efforts of researchers and cybersecurity professionals to concentrate much on the security and privacy of sensitive data in cloud computing.

With the popularity of Internet of Things (IoT) and cloud computing technologies, mobile healthcare (mHealth) can offer remote, accurate, and effective medical services for patients according to their personal health records (PHRs). However, data security and efficient access of the PHR should be addressed. Attribute-based encryption (ABE) is regarded as a well-received cryptographic mechanism to simultaneously realize fine-grained access control and data confidentiality in mHealth. Nevertheless, existing works are either constructed in the single-authority setting which may be a performance bottleneck, or lack of efficient user decryption. In this paper, we propose SEMAAC, a secure and efficient multi-authority access control system for IoT-enabled mHealth. In SEMAAC, there are multiple independently worked attribute authorities (AAs). A new entity could be an AA without re-building the system. To reduce the user decryption overhead, most decryption is executed in cloud server, which whereafter returns a partial decryption ciphertext (PDC). The AAs can help the user to check if the PDC is correctly computed. Additionally, a restricted user can delegate his/her key to someone to outsource the decryption and check the returned result, without exposing the plaintext PHR file. The proposed SEMAAC is proved to be adaptively secure in the standard model. The numerical analysis and extensive experiments illustrate the efficiency and advantage of our scheme.

Patient Health Record (PHR) is a complete Patient related health data, which is getting stored in cloud computing to gain cost benefit and better access control.In maintaining PHR, cloud computing plays an inevitable role, since small hospitals and physicians are not affordable to maintain own servers to keep PHRs for cost and security reasons .Providing accessibility to variable stake holders like Patients, Physicians, Hospitals and Insurance Providers, become a tedious process in private individual servers with encryption mechanisms.Cloud solution ensures that PHRs ' availability to the necessary stake holders at any point of time.In any country , there are laws which governs to maintain privacy of patient health records, and hence maintaining PHRs in cloud are subjected to privacy concerns and high risk of getting exploited.There are various encryption schemes exist to preserve PHR's security and privacy in Cloud computing.In this paper, we propose a secure PHR accessing scheme for dynamic environment to preserve privacy in semi trusted cloud servers .

This study will investigate the security problems that are linked with the Hadoop ecosystem, Big Data, Map Reduce, and cloud computing. The objective of this research is to analyze these concerns. Specifically, the primary emphasis is placed on the security concerns that are linked with big data in cloud computing. Big data applications have a lot to offer organizations, companies, and a broad range of industries, no matter how big or little. There have been issues with Hadoop and cloud computing security, therefore we also look at several possible solutions to these difficulties. The field of cloud computing security is experiencing fast growth in several areas, including computer security, network security, information security, and data privacy. Data, apps, and the infrastructure related to them are greatly helped by cloud computing's use of rules, technologies, controls, and big data tools to keep everything safe. Researchers also anticipate that cloud computing, big data, and their associated benefits will represent the most exciting new areas of study.

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In the cloud, for achieving access control and keeping data confidential, the data owners could adopt attribute-based encryption to encrypt the stored data. Users with limited computing power are however more likely to delegate the mask of the decryption task to the cloud servers to reduce the computing cost. As a result, attribute-based encryption with delegation emerges. Still, there are caveats and questions remaining in the previous relevant works. For instance, during the delegation, the cloud servers could tamper or replace the delegated ciphertext and respond a forged computing result with malicious intent. They may also cheat the eligible users by responding them that they are ineligible for the purpose of cost saving. Furthermore, during the encryption, the access policies may not be flexible enough as well. Since policy for general circuits enables to achieve the strongest form of access control, a construction for realizing circuit ciphertext-policy attribute-based hybrid encryption with verifiable delegation has been considered in our work. In such a system, combined with verifiable computation and encrypt-then-mac mechanism, the data confidentiality, the fine-grained access control and the correctness of the delegated computing results are well guaranteed at the same time. Besides, our scheme achieves security against chosen-plaintext attacks under the k-multilinear Decisional Diffie-Hellman assumption. Moreover, an extensive simulation campaign confirms the feasibility and efficiency of the proposed solution.

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