Privacy-preserving data outsourcing in the cloud via semantic data splitting

This work intends to introduce an optimization-based privacy preservation model via selecting the optimal key matrix. Here, privacy preservation is carried out under two processes, namely, "data sanitization and restoration." In fact, data sanitization is the data preservation method, where the data (message) are preserved using the optimal key. Similarly, data restoration is the inverse procedure of sanitization. Here, the key matrix is optimally chosen using a novel hybrid algorithm. For optimization purpose, this work deploys a hybrid optimization approach known as random-based grey dragon algorithm (R-GDA) that involves the concepts of both "dragonfly algorithm (DA) and grey wolf optimization (GWO) algorithm." The novelty of the work is introduced in hybrid optimization approach R-GDA. Eventually, the supremacy of the adopted method is validated over other existing approaches in terms of various measures such as privacy, utility, and so on. The privacy preservation in the cloud is achievable in the field of education, banking sector, military, and the research community.

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“…Furthermore, the key coefficients are multiplied with partial data matrix of i/p data as specified in Equation (5).…”

Section: Constructing Retrievable Databasementioning

confidence: 99%

“…In addition, the cloud platform permits users to store, edit and recover a huge quantity of data. Owing to extensive appliances offered by cloud paradigm, private firms, and official firms access the cloud platforms for varied applications 4,5 …”

Section: Introductionmentioning

confidence: 99%

Hybrid optimization‐based privacy preservation of database publishing in cloud environment

Doss

Kamalakkannan

2022

Concurrency and Computation

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“…Semantically-grounded splitting mechanism is well-suited for unstructured data such as textual data, it can provide keyword search for online document, email, and messaging applications. For example, a recent work by [59] automatically detects and splits the sets of textual entities that may disclose sensitive information by analysing the semantics they convey and their semantic dependencies. Attribute-level splitting such as vertical splitting [2] is very useful for statistical databases because usually is the combination of several risky attributes that may lead to personal re-identification.…”

Section: Related Workmentioning

confidence: 99%

Protecting Big Data Privacy Using Randomized Tensor Network Decomposition and Dispersed Tensor Computation

Ong,

Ng,

Tjuawinata

et al. 2021

Preprint

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Data privacy is an important issue for organizations and enterprises to securely outsource data storage, sharing, and computation on clouds / fogs. However, data encryption is complicated in terms of the key management and distribution; existing secure computation techniques are expensive in terms of computational / communication cost and therefore do not scale to big data computation. Tensor network decomposition and distributed tensor computation have been widely used in signal processing and machine learning for dimensionality reduction and large-scale optimization. However, the potential of distributed tensor networks for big data privacy preservation have not been considered before, this motivates the current study. Our primary intuition is that tensor network representations are mathematically non-unique, unlinkable, and uninterpretable; tensor network representations naturally support a range of multilinear operations for compressed and distributed / dispersed computation. Therefore, we propose randomized algorithms to decompose big data into randomized tensor network representations and analyze the privacy leakage for 1D to 3D data tensors. The randomness mainly comes from the complex structural information commonly found in big data; randomization is based on controlled perturbation applied to the tensor blocks prior to decomposition. The distributed tensor representations are dispersed on multiple clouds / fogs or servers / devices with metadata privacy, this provides both distributed trust and management to seamlessly secure big data storage, communication, sharing, and computation. Experiments show that the proposed randomization techniques are helpful for big data anonymization and efficient for big data storage and computation.

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“…The Proposed Scheme. To verify the integrity for shared data efficiently [15,[25][26][27][28][29][30], our scheme is designed to achieve the following goals.…”

Section: System Architecturementioning

confidence: 99%

Integrity Audit of Shared Cloud Data with Identity Tracking

Yun

et al. 2019

Security and Communication Networks

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More and more users are uploading their data to the cloud without storing any copies locally. Under the premise that cloud users cannot fully trust cloud service providers, how to ensure the integrity of users’ shared data in the cloud storage environment is one of the current research hotspots. In this paper, we propose a secure and effective data sharing scheme for dynamic user groups. (1) In order to realize the user identity tracking and the addition and deletion of dynamic group users, we add a new role called Rights Distribution Center (RDC) in our scheme. (2) To protect the privacy of user identity, when performing third party audit to verify data integrity, it is not possible to determine which user is a specific user. Therefore, the fairness of the audit can be promoted. (3) Define a new integrity audit model for shared cloud data. In this scheme, the user sends the encrypted data to the cloud and the data tag to the Rights Distribution Center (RDC) by using data blindness technology. Finally, we prove the security of the scheme through provable security theory. In addition, the experimental data shows that our proposed scheme is more efficient and scalable than the state-of-the-art solution.

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Privacy-preserving data outsourcing in the cloud via semantic data splitting

Cited by 31 publications

References 29 publications

Hybrid optimization‐based privacy preservation of database publishing in cloud environment

Hybrid optimization‐based privacy preservation of database publishing in cloud environment

Protecting Big Data Privacy Using Randomized Tensor Network Decomposition and Dispersed Tensor Computation

Integrity Audit of Shared Cloud Data with Identity Tracking

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