Achieving Fine-grained Multi-keyword Ranked Search over Encrypted Cloud Data

With the advancement of Cloud computing, people now store their data on remote Cloud servers for larger computation and storage resources. However, users’ data may contain sensitive information of users and should not be disclosed to the Cloud servers. If users encrypt their data and store the encrypted data in the servers, the search capability supported by the servers will be significantly reduced because the server has no access to the data content. In this paper, we propose a Fine-grained Multi-keyword Ranked Search (FMRS) scheme over encrypted Cloud data. Specifically, we leverage novel techniques to realize multikeyword ranked search, which supports both mixed “AND”, “OR” and “NO” operations of keywords and ranking according to the preference factor and relevance score. Through security analysis, we can prove that the data confidentiality, privacy protection of index and trapdoor, and the unlinkability of trapdoor can be achieved in our FMRS. Besides, Extensive experiments show that the FMRS possesses better performance than existing schemes in terms of functionality and efficiency

Efficient tree structured algorithm for providing confidentiality of location data to minimize communication overhead in LBS Services

We present an effective and protection safeguarding polygons spatial inquiry structure for area based administrations, called Polaris. With Polaris, the LBS supplier redistributes the encoded LBS information to cloud server, and the enrolled client can question any polygon range to get precise LBS results without revealing his/her inquiry data to the LBS supplier and cloud server. Proficient uncommon polygons spatial inquiry calculation over ciphertext is developed dependent on an enhanced homomorphic encryption innovation over Composite request gathering. With SPSQ, Polaris can look re-appropriated scrambled LBS information in cloud server by the encoded demand, and react the scrambled polygons spatial question results precisely

A parallelized disjunctive query based searchable encryption scheme for big data

Searchable Encryption (SE) allows a client to search over large amounts of encrypted data outsourced to the Cloud. Although, this helps to maintain the confidentiality of the outsourced data but achieving privacy is a difficult and resource intensive task. With the increase in the query effectiveness, i.e., by shifting from single keyword SE to multi-keyword SE there is a notable drop in the efficiency. This motivates to make use of the advances in the multi-core architectures and multiple threads where the search can be delegated across different threads to perform search in a parallel fashion. The proposed scheme is based on probabilistic trapdoors that are formed by making use of the property of modular inverses. The use of probabilistic trapdoors helps resist distinguishability attacks. The rigorous security analysis helps us to appreciate the advantage of having a probabilistic trapdoor. Furthermore, to validate the performance of the proposed scheme, it is implemented and deployed onto the British Telecommunication's Public Cloud offering and tested over a real speech corpus. The implementation is also extended to anticipate the performance gain by using the multi-core architecture that helps to maintain the lightweight property of the scheme

ABDKS Attribute-Based Encryption with Dynamic Keyword Search in Fog Computing

Attribute-based encryption with keyword search (ABKS) achieves both fine-grained access control and keyword search. However, in the previous ABKS schemes, the search algorithm requires that each keyword between the target keyword set and the ciphertext keyword set be the same, otherwise the algorithm doesn\u27t output any search result, which is not conducive to use. Moreover, the previous ABKS schemes are vulnerable to what we call a \emph{peer-decryption attack}, that is, the ciphertext may be eavesdropped and decrypted by an adversary who has sufficient authorities but no information about the ciphertext keywords. In this paper, we provide a new system in fog computing, the ciphertext-policy attribute-based encryption with dynamic keyword search (ABDKS). In ABDKS, the search algorithm requires only \emph{one} keyword to be identical between the two keyword sets and outputs the corresponding correlation which reflects the number of the same keywords in those two sets. In addition, our ABDKS is resistant to peer-decryption attack, since the decryption requires not only sufficient authority but also at least one keyword of the ciphertext. Beyond that, the ABDKS shifts most computational overheads from resource constrained users to fog nodes. The security analysis shows that the ABDKS can resist Chosen-Plaintext Attack (CPA) and Chosen-Keyword Attack (CKA)

A review of the state of the art in privacy and security in the eHealth cloud

The proliferation and usefulness of cloud computing in eHealth demands high levels of security and privacy for health records. However, eHealth clouds pose serious security and privacy concerns for sensitive health data. Therefore, practical and effective methods for security and privacy management are essential to preserve the privacy and security of the data. To review the current research directions in security and privacy in eHealth clouds, this study has analysed and summarized the state of the art technologies and approaches reported in security and privacy in the eHealth cloud. An extensive review covering 132 studies from several peer-reviewed databases such as IEEE Xplore was conducted. The relevant studies were reviewed and summarized in terms of their benefits and risks. This study also compares several research works in the domain of data security requirements. This paper will provide eHealth stakeholders and researchers with extensive knowledge and information on current research trends in the areas of privacy and security

AI-enabled privacy-preservation phrase with multi-keyword ranked searching for sustainable edge-cloud networks in the era of industrial IoT

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