PAbAC : a privacy preserving attribute based framework for fine grained access control in clouds

Several existing access control solutions mainly focus on preserving confidentiality of stored data from unauthorized access and the storage provider. Moreover, to keep sensitive user data confidential against untrusted servers, existing solutions usually apply cryptographic methods by disclosing data decryption keys only to authorized users. However, these solutions inevitably introduce a heavy computation overhead on the data owner for key distribution and data management when fine-grained data access control is desired. In addition, access control policies as well as users’ access patterns are also considered as sensitive information that should be protected from the cloud. In this paper, we propose PAbAC, a novel privacy preserving Attribute-based framework, that combines attribute-based encryption and attribute-based signature mechanisms for securely sharing outsourced data via the public cloud. Our proposal is multifold. First, it ensures fine-grained cryptographic access control enforced at the data owner’s side, while providing the desired expressiveness of the access control policies. Second, PAbAC preserves users’ privacy, while hiding any identifying information used to satisfy the access control. Third, PAbAC is proven to be highly scalable and efficient for sharing outsourced data in remote servers, at both the client and the cloud provider side

PHOABE : securely outsourcing multi-authority attribute based encryption with policy hidden for cloud assisted IoT

Attribute based encryption (ABE) is an encrypted access control mechanism that ensures efficient data sharing among dynamic group of users. Nevertheless, this encryption technique presents two main drawbacks, namely high decryption cost and publicly shared access policies, thus leading to possible users’ privacy leakage. In this paper, we introduce PHOABE, a Policy-Hidden Outsourced ABE scheme. Our construction presents several advantages. First, it is a multi-attribute authority ABE scheme. Second, the expensive computations for the ABE decryption process is partially delegated to a Semi Trusted Cloud Server. Third, users’ privacy is protected thanks to a hidden access policy. Fourth, PHOABE is proven to be selectively secure, verifiable and policy privacy preserving under the random oracle model. Five, estimation of the processing overhead proves its feasibility in IoT constrained environments

SECURING THE DATA STORAGE AND PROCESSING IN CLOUD COMPUTING ENVIRONMENT

Organizations increasingly utilize cloud computing architectures to reduce costs and en- ergy consumption both in the data warehouse and on mobile devices by better utilizing the computing resources available. However, the security and privacy issues with publicly available cloud computing infrastructures have not been studied to a sufficient depth for or- ganizations and individuals to be fully informed of the risks; neither are private nor public clouds prepared to properly secure their connections as middle-men between mobile de- vices which use encryption and external data providers which neglect to encrypt their data. Furthermore, cloud computing providers are not well informed of the risks associated with policy and techniques they could implement to mitigate those risks. In this dissertation, we present a new layered understanding of public cloud comput- ing. On the high level, we concentrate on the overall architecture and how information is processed and transmitted. The key idea is to secure information from outside attack and monitoring. We use techniques such as separating virtual machine roles, re-spawning virtual machines in high succession, and cryptography-based access control to achieve a high-level assurance of public cloud computing security and privacy. On the low level, we explore security and privacy issues on the memory management level. We present a mechanism for the prevention of automatic virtual machine memory guessing attacks

Constant-size threshold attribute based SignCryption for cloud applications

In this paper, we propose a novel constant-size threshold attribute-based signcryption scheme for securely sharing data through public clouds. Our proposal has several advantages. First, it provides flexible cryptographic access control, while preserving users’ privacy as the identifying information for satisfying the access control policy are not revealed. Second, the proposed scheme guarantees both data origin authentication and anonymity thanks to the novel use of attribute based signcryption mechanism, while ensuring the unlinkability between the different access sessions. Third, the proposed signcryption scheme has efficient computation cost and constant communication overhead whatever the number of involved attributes. Finally, our scheme satisfies strong security properties in the random oracle model, namely Indistinguishability against the Adaptive Chosen Ciphertext Attacks (IND-CCA2), Existential Unforgeability against Chosen Message Attacks (EUFCMA) and privacy preservation of the attributes involved in the signcryption process, based on the assumption that the augmented Multi-Sequence of Exponents Decisional Diffie-Hellman (aMSE-DDH) problem and the Computational Diffie Hellman Assumption (CDH) are hard

A Hybrid Multi-user Cloud Access Control based Block Chain Framework for Privacy Preserving Distributed Databases

Most of the traditional medical applications are insecure and difficult to compute the data integrity with variable hash size. Traditional medical data security systems are insecure and it depend on static parameters for data security. Also, distributed based cloud storage systems are independent of integrity computational and data security due to unstructured data and computational memory. As the size of the data and its dimensions are increasing in the public and private cloud servers, it is difficult to provide the machine learning based privacy preserving in cloud computing environment. Block-chain technology plays a vital role for large cloud databases. Most of the conventional block-chain frameworks are based on the existing integrity and confidentiality models. Also, these models are based on the data size and file format. In this model, a novel integrity verification and encryption framework is designed and implemented in cloud environment.  In order to overcome these problems in the cloud computing environment, a hybrid integrity and security-based block-chain framework is designed and implemented on the large distributed databases. In this framework,a novel decision tree classifier is used along with non-linear mathematical hash algorithm and advanced attribute-based encryption models are used to improve the privacy of multiple users on the large cloud datasets. Experimental results proved that the proposed advanced privacy preserving based block-chain technology has better efficiency than the traditional block-chain based privacy preserving systems on large distributed databases

Access of Encrypted Personal Record in Cloud

Personal record is a data, which is collected and stored in cloud computing to gain cost benefit and better access control. In maintaining Personal Record, cloud computing plays an important role, since minor organizations are not affordable to keep own servers to maintain the personal record for cost and security aims. Providing availability to various stake holders become a deadly process in isolated individual servers with encryption technology. Cloud ensures that personal record availability to the necessary user at any point of time. In any country, there is a law which governs to maintain privacy of special records, and hence maintaining recodes in cloud are subjected to privacy concerns and high risk of getting exploited. There are various encryption schemes to provide personal records security and privacy in Cloud computing. Extensive logical and experimental results are presented which show the security, scalability and efficiency of our proposed scheme. DOI: 10.17762/ijritcc2321-8169.15016