Aggregation-Based Tag Deduplication for Cloud Storage with Resistance against Side Channel Attack

Tang, Xin; Zhou, Linna; Hu, Bingwei; Wu, Haowen

doi:10.1155/2021/6686281

Cited by 8 publications

(3 citation statements)

References 19 publications

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“…Tang et al . [15] proposed a secure de-duplication scheme based on threshold re-encryption, which can resist sidechannel attacks while effectively reducing computational overhead. Gao et al .…”

Section: A Related Workmentioning

confidence: 99%

Data Secure De-Duplication and Recovery Based on Public Key Encryption With Keyword Search

Zheng

Zhang

et al. 2023

IEEE Access

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In the current era of information explosion, users' demand for data storage is increasing, and data on the cloud has become the first choice of users and enterprises. Cloud storage facilitates users to backup and share data, effectively reducing users' storage expenses. As the duplicate data of different users are stored multiple times, leading to a sudden decrease in storage utilization of cloud servers. Data stored in plaintext form can directly remove duplicate data, while cloud servers are semi-trusted and usually need to store data after encryption to protect user privacy. In this paper, we focus on how to achieve secure de-duplication and recover data in ciphertext for different users, and determine whether the indexes of public key searchable encryption and the matching relationship of trapdoor are equal in ciphertext to achieve secure de-duplication. For the duplicate file, the data user's re-encryption key about the file is appended to the ciphertext chain table of the stored copy. The cloud server uses the re-encryption key to generate the specified transformed ciphertext, and the data user decrypts the transformed ciphertext by its private key to recover the file. The proposed scheme is secure and efficient through security analysis and experimental simulation analysis.

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Section: A Related Workmentioning

confidence: 99%

Data Secure De-Duplication and Recovery Based on Public Key Encryption With Keyword Search

Zheng

Zhang

et al. 2023

IEEE Access

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“…A secure aggregation-based tag deduplication method (ATDS) was designed in [15] for side channel attack recognition through the public verification. The Lagrangian interpolation-based aggregation method was developed to attain tag deduplication.…”

Section: Related Workmentioning

confidence: 99%

Bgnba-oco Based Privacy Preserving Attribute Based Access Control With Data Duplication for Secure Storage in Cloud

Pavithra

Prakash

Vennila³

2023

Preprint

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Cloud computing technology offers flexible and expedient services which carry a variety of profits for both societies as well as individuals. In the epoch of data explosion, customers and business establishments require to store enormous data. The exception of cloud service advantages, data confidentiality becomes a challenging aspect to be covered as far as executing data access control. The implementation of Attribute-based Encryption in a Cloud Computing (CC) environment solves the demanding topic of outsourced data enabling fine-grained and pliable access control. In this work, a method called Boneh Goh Nissim Bilinear Attribute-based Optimal Cache Oblivious (BGNBA-OCO) access control and secure de-duplication for data storage in cloud computing is proposed. First, Boneh Goh Nissim Privacy Preserving Revocable Attribute-based Encryption is designed that reinforces attribute revocation and avert the discharge of sensitive information. Moreover, to prevent disclosure of access patterns Optimal Cache Oblivious algorithm is employed to hide the access patterns in cloud storage using rand pattern matching. It also supports updating both encrypted data and access control policies to minimize communication and computation overhead of data duplication and encryption processes concurrently. In this way, secure data sharing is performed to achieve higher data confidentiality and integrity. The analyzed results indicate that the BGNBA-OCO method offers an efficient solution in terms of achieving higher data confidentiality, and integrity, and minimizing the communication as well as computation overhead than the conventional techniques.

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“…However, when uploading data to the CS, users want to encrypt the data to protect their privacy and prevent the data content from being obtained by CS or other attackers [5], [6]. However, with traditional encryption schemes, users randomly select keys and encrypt the plaintext.…”

mentioning

confidence: 99%

A Data Deduplication Scheme Based on DBSCAN With Tolerable Clustering Deviation

Teng

Xian

Lu³

et al. 2023

IEEE Access

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To protect data privacy, users prefer to store encrypted data in cloud servers. Cloud servers reduce the cost of storage and network bandwidth by eliminating duplicate copies. To address the potential internal data leakage problem, the concept of clustering deviation is proposed for the first time. We improve the DBSCAN algorithm to tolerate clustering deviation. A data deduplication scheme is built upon the new algorithm, which considers users as clustering samples. Instead of immediately re-clustering new users, a certain deviation is tolerated to assign the users to the existing classes. We determine the popularity of the data according to user clustering results and apply different encryption schemes to protect the security of unpopular data more effectively. The performance of the algorithm is analyzed and compared with other methods through experiments, and the results verify the feasibility and efficiency of the proposed deduplication scheme.

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Aggregation-Based Tag Deduplication for Cloud Storage with Resistance against Side Channel Attack

Cited by 8 publications

References 19 publications

Data Secure De-Duplication and Recovery Based on Public Key Encryption With Keyword Search

Data Secure De-Duplication and Recovery Based on Public Key Encryption With Keyword Search

Bgnba-oco Based Privacy Preserving Attribute Based Access Control With Data Duplication for Secure Storage in Cloud

A Data Deduplication Scheme Based on DBSCAN With Tolerable Clustering Deviation

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