Fine-Grained Access Control Systems Suitable for Resource-Constrained Users in Cloud Computing

For the sake of practicability of cloud computing, fine-grained data access is frequently required in the sense that users with different attributes should be granted different levels of access privileges. However, most of existing access control solutions are not suitable for resource-constrained users because of large computation costs, which linearly increase with the complexity of access policies. In this paper, we present an access control system based on ciphertext-policy attribute-based encryption. The proposed access control system enjoys constant computation cost and is proven secure in the random oracle model under the decision Bilinear Diffie-Hellman Exponent assumption. Our access control system supports AND-gate access policies with multiple values and wildcards, and it can efficiently support direct user revocation. Performance comparisons indicate that the proposed solution is suitable for resource-constrained environment

CUPS : Secure Opportunistic Cloud of Things Framework based on Attribute Based Encryption Scheme Supporting Access Policy Update

The ever‐growing number of internet connected devices, coupled with the new computing trends, namely within emerging opportunistic networks, engenders several security concerns. Most of the exchanged data between the internet of things (IoT) devices are not adequately secured due to resource constraints on IoT devices. Attribute‐based encryption is a promising cryptographic mechanism suitable for distributed environments, providing flexible access control to encrypted data contents. However, it imposes high decryption costs, and does not support access policy update, for highly dynamic environments. This paper presents CUPS, an ABE‐based framework for opportunistic cloud of things applications, that securely outsources data decryption process to edge nodes in order to reduce the computation overhead on the user side. CUPS allows end‐users to offload most of the decryption overhead to an edge node and verify the correctness of the received partially decrypted data from the edge node. Moreover, CUPS provides the access policy update feature with neither involving a proxy‐server, nor re‐encrypting the enciphered data contents and re‐distributing the users' secret keys. The access policy update feature in CUPS does not affect the size of the message received by the end‐user, which reduces the bandwidth and the storage usage. Our comprehensive theoretical analysis proves that CUPS outperforms existing schemes in terms of functionality, communication and computation overheads

A HYBRIDIZED ENCRYPTION SCHEME BASED ON ELLIPTIC CURVE CRYPTOGRAPHY FOR SECURING DATA IN SMART HEALTHCARE

Recent developments in smart healthcare have brought us a great deal of convenience. Connecting common objects to the Internet is made possible by the Internet of Things (IoT). These connected gadgets have sensors and actuators for data collection and transfer. However, if users' private health information is compromised or exposed, it will seriously harm their privacy and may endanger their lives. In order to encrypt data and establish perfectly alright access control for such sensitive information, attribute-based encryption (ABE) has typically been used. Traditional ABE, however, has a high processing overhead. As a result, an effective security system algorithm based on ABE and Fully Homomorphic Encryption (FHE) is developed to protect health-related data. ABE is a workable option for one-to-many communication and perfectly alright access management of encrypting data in a cloud environment. Without needing to decode the encrypted data, cloud servers can use the FHE algorithm to take valid actions on it. Because of its potential to provide excellent security with a tiny key size, elliptic curve cryptography (ECC) algorithm is also used. As a result, when compared to related existing methods in the literature, the suggested hybridized algorithm (ABE-FHE-ECC) has reduced computation and storage overheads. A comprehensive safety evidence clearly shows that the suggested method is protected by the Decisional Bilinear Diffie-Hellman postulate. The experimental results demonstrate that this system is more effective for devices with limited resources than the conventional ABE when the system’s performance is assessed by utilizing standard model

An Innovative Approach for Enhancing Cloud Data Security using Attribute based Encryption and ECC

Cloud computing is future for upcoming generations. Nowadays various companies are looking to use Cloud computing services, as it may benefit them in terms of price, reliability and unlimited storage capacity. Providing security and privacy protection for the cloud data is one of the most difficult task in recent days. One of the measures which customers can take care of is to encrypt their data before it is stored on the cloud. Recently, the attribute-based encryption (ABE) is a popular solution to achieve secure data transmission and storage in the cloud computing. In this paper, an efficient hybrid approach using attribute-based encryption scheme and ECC is proposed to enhance the security and privacy issues in cloud. Here, the proposed scheme is based on Cipher text-Policy Attribute Based Encryption (CP-ABE) without bilinear pairing operations. In this approach, the computation-intensive bilinear pairing operation is replaced by the scalar multiplication on elliptic curves. Experimental results show that the proposed scheme has good cryptographic properties, and high security level which depends in the difficulty to solve the discrete logarithm problem on elliptic curves (ECDLP)

Novel Techniques for Secure Use of Public Cloud Computing Resources

The federal government has an expressed interest in moving data and services to third party service providers in order to take advantage of the flexibility, scalability, and potential cost savings. This approach is called cloud computing. The thesis for this research is that efficient techniques exist to support the secure use of public cloud computing resources by a large, federated enterprise. The primary contributions of this research are the novel cryptographic system MA-AHASBE (Multi-Authority Anonymous Hierarchical Attribute-Set Based Encryption), and the techniques used to incorporate MA-AHASBE in a real world application. Performance results indicate that while there is a cost associated with enforcing the suggested security model, the cost is not unreasonable and the benefits in security can be significant. The contributions of this research give the DoD additional tools for supporting the mission while taking advantage of the cost efficient public cloud computing resources that are becoming widely available

Efficient User-Centric Privacy-Friendly and Flexible Wearable Data Aggregation and Sharing

Wearable devices can offer services to individuals and the public. However, wearable data collected by cloud providers may pose privacy risks. To reduce these risks while maintaining full functionality, healthcare systems require solutions for privacy-friendly data processing and sharing that can accommodate three main use cases: (i) data owners requesting processing of their own data, and multiple data requesters requesting data processing of (ii) a single or (iii) multiple data owners. Existing work lacks data owner access control and does not efficiently support these cases, making them unsuitable for wearable devices. To address these limitations, we propose a novel, efficient, user-centric, privacy-friendly, and flexible data aggregation and sharing scheme, named SAMA. SAMA uses a multi-key partial homomorphic encryption scheme to allow flexibility in accommodating the aggregation of data originating from a single or multiple data owners while preserving privacy during the processing. It also uses ciphertext-policy attribute-based encryption scheme to support fine-grain sharing with multiple data requesters based on user-centric access control. Formal security analysis shows that SAMA supports data confidentiality and authorisation. SAMA has also been analysed in terms of computational and communication overheads. Our experimental results demonstrate that SAMA supports privacy-reserving flexible data aggregation more efficiently than the relevant state-of-the-art solutions