Preserving Privacy of Co-occurring Keywords over Encrypted Data

Background: Cloud storage is generally used to provide on-demand services with sufficient scalability in an efficient network environment, and various encryption algorithms are typically applied to protect the data in the cloud. However, it is non-trivial to obtain the original data after encryption and efficient methods are needed to access the original data. Methods: In this paper, we propose a new user-controlled and efficient encrypted data sharing model in cloud storage. It preprocesses user data to ensure the confidentiality and integrity based on triple encryption scheme of CP-ABE ciphertext access control mechanism and integrity verification. Moreover, it adopts secondary screening program to achieve efficient ciphertext retrieval by using distributed Lucene technology and fine-grained decision tree. In this way, when a trustworthy third party is introduced, the security and reliability of data sharing can be guaranteed. To provide data security and efficient retrieval, we also combine active user with active system. Results: Experimental results show that the proposed model can ensure data security in cloud storage services platform as well as enhance the operational performance of data sharing. Conclusion: The proposed security sharing mechanism works well in an actual cloud storage environment.

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Cryptosystem Conversion, Packing and Matrix Processing of Homomorphically Encrypted Data: Application to IOT Devices

IEEE Access ◽

10.1109/access.2021.3058849 ◽

2021 ◽

Vol 9 ◽

pp. 28302-28316

Author(s):

Maxime Pistono ◽

Reda Bellafqira ◽

Gouenou Coatrieux

Keyword(s):

Encrypted Data ◽

Data Application ◽

Iot Devices

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What If Keys Are Leaked? towards Practical and Secure Re-Encryption in Deduplication-Based Cloud Storage

Information ◽

10.3390/info12040142 ◽

2021 ◽

Vol 12 (4) ◽

pp. 142

Author(s):

Weijing You ◽

Lei Lei ◽

Bo Chen ◽

Limin Liu

Keyword(s):

Experimental Evaluation ◽

Cloud Storage ◽

Security Analysis ◽

Encrypted Data ◽

Cloud Data ◽

Storage Cost ◽

Outsourced Data ◽

Proof Of Ownership ◽

Client Side ◽

Over Time

By only storing a unique copy of duplicate data possessed by different data owners, deduplication can significantly reduce storage cost, and hence is used broadly in public clouds. When combining with confidentiality, deduplication will become problematic as encryption performed by different data owners may differentiate identical data which may then become not deduplicable. The Message-Locked Encryption (MLE) is thus utilized to derive the same encryption key for the identical data, by which the encrypted data are still deduplicable after being encrypted by different data owners. As keys may be leaked over time, re-encrypting outsourced data is of paramount importance to ensure continuous confidentiality, which, however, has not been well addressed in the literature. In this paper, we design SEDER, a SEcure client-side Deduplication system enabling Efficient Re-encryption for cloud storage by (1) leveraging all-or-nothing transform (AONT), (2) designing a new delegated re-encryption (DRE), and (3) proposing a new proof of ownership scheme for encrypted cloud data (PoWC). Security analysis and experimental evaluation validate security and efficiency of SEDER, respectively.

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Privacy-Preserving Deep Neural Network Methods: Computational and Perceptual Methods—An Overview

Electronics ◽

10.3390/electronics10111367 ◽

2021 ◽

Vol 10 (11) ◽

pp. 1367

Author(s):

Raghida El El Saj ◽

Ehsan Sedgh Sedgh Gooya ◽

Ayman Alfalou ◽

Mohamad Khalil

Keyword(s):

Neural Networks ◽

Classification Accuracy ◽

Deep Neural Networks ◽

Privacy Preserving ◽

Sensitive Data ◽

Encrypted Data ◽

High Classification Accuracy ◽

Hide Information ◽

Network Methods ◽

Cloud Environments

Privacy-preserving deep neural networks have become essential and have attracted the attention of many researchers due to the need to maintain the privacy and the confidentiality of personal and sensitive data. The importance of privacy-preserving networks has increased with the widespread use of neural networks as a service in unsecured cloud environments. Different methods have been proposed and developed to solve the privacy-preserving problem using deep neural networks on encrypted data. In this article, we reviewed some of the most relevant and well-known computational and perceptual image encryption methods. These methods as well as their results have been presented, compared, and the conditions of their use, the durability and robustness of some of them against attacks, have been discussed. Some of the mentioned methods have demonstrated an ability to hide information and make it difficult for adversaries to retrieve it while maintaining high classification accuracy. Based on the obtained results, it was suggested to develop and use some of the cited privacy-preserving methods in applications other than classification.

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