CryptoGA: a cryptosystem based on genetic algorithm for cloud data security

Tahir, Muhammad; Sardaraz, Muhammad; Mehmood, Zahid; Muhammad, Shakoor

doi:10.1007/s10586-020-03157-4

Cited by 87 publications

(58 citation statements)

References 51 publications

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“…This model focuses on condition-based monitoring securely. CryptoGA (GA) encryption is a Genetic cryptographic algorithm has been discussed by Muhammad Tahir et al [ 29 ]. The GA compared with some algorithms, which denoted the effective transmission rate.…”

Section: Related Workmentioning

confidence: 99%

Achieving data privacy for decision support systems in times of massive data sharing

et al. 2022

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The world is suffering from a new pandemic of Covid-19 that is affecting human lives. The collection of records for Covid-19 patients is necessary to tackle that situation. The decision support systems (DSS) are used to gather that records. The researchers access the patient’s data through DSS and perform predictions on the severity and effect of the Covid-19 disease; in contrast, unauthorized users can also access the data for malicious purposes. For that reason, it is a challenging task to protect Covid-19 patient data. In this paper, we proposed a new technique for protecting Covid-19 patients’ data. The proposed model consists of two folds. Firstly, Blowfish encryption uses to encrypt the identity attributes. Secondly, it uses Pseudonymization to mask identity and quasi-attributes, then all the data links with one another, such as the encrypted, masked, sensitive, and non-sensitive attributes. In this way, the data becomes more secure from unauthorized access.

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Section: Related Workmentioning

confidence: 99%

Achieving data privacy for decision support systems in times of massive data sharing

et al. 2022

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“…Tahir et al [ 24 ] introduced the CryptoGenetic algorithm (CGA) for solving problems in security. This method contains two modes of working; downloading and uploading data from storage in the cloud.…”

Section: Related Workmentioning

confidence: 99%

A blockchain and smart contract-based data provenance collection and storing in cloud environment

Jyoti

Chauhan

2022

Wireless Netw

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Data uploading needs security and privacy in the cloud. But there are some problems like centralized provenance data (PD) collection, storage, lack of security, integrity, and more time consumption. There are methods like Rabin, Knapsack, McEliece, Elagamal, and Rivest–Shamir–Adleman for the generation of keys but it increases the encryption and decryption time and less security. Therefore, the blockchain and smart contract-based data provenance (BSCDP) Architecture is proposed for providing secure storage in the cloud environment. Initially, the fingerprint biometrics and physically uncloneable functions (PUF) have been used in verification process. The combination of PUF and fingerprint biometrics is used for secure data transmission. To protect privacy and strengthen the security, the fuzzy extractor is employed. Secondly, we use an elliptic-curve key based cyclic shift transposition cryptography algorithm for enhancing the security when sharing the key. Thirdly, we introduce the blockchain and the interplanetary file system (IPFS) for PD collection, hash computation, and storing with reduced computational overhead (CO). The integrity of data is maintained by using blockchain based secure hashing algorithm-3. By arranging fuzzy based smart contracts (FSC), the data user (DU) tracks their data. FSC is employed for tracking the history of data. The data collected is directly stored in IPFS and the DU gets a hash from IPFS to retrieve the data in the future. Finally, the data verification is done by the provenance auditor. When comparing our proposed BSCDP method with existing methods, the proposed BSCDP method achieves high security in the cloud environment for 2 K users in terms of evidence insertion time (30 ms), verification time (30 ms), response time (40 ms), total change rate (5%), CO (5.8 Kb), encryption (50 ms) and decryption time (52 ms).

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“…A new technique based on a genetic algorithm (GA) was proposed in [16] to deal with data integrity and privacy concerns in cloud market. Keys for encryption and decryption are generated using GA.…”

Section: Related Workmentioning

confidence: 99%

A Novel Scheme for Malicious Nodes Detection in Cloud Markets Based on Fuzzy Logic Technique

Alsarhan

Al-Ghuwairi²,

Alshdaifat³

et al. 2022

Int. J. Interact. Mob. Technol.

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Cloud security vulnerabilities have recently become more prevalent around the world, posing a threat to cloud service providers' (CSPs) ability to respond to client demands. In cloud market, the requests are announced by the client nodes to their CSP. A malicious node can alter a client's request, resulting in the next cloud market collapse, decreased reliability, and data leaking.To identify malicious nodes in the cloud market, a novel fuzzy multiple criterion decision making scheme is suggested. Authentication test, trust level, traffic size, and node activity levels are all taken into consideration simultaneously as the major criteria for identifying malicious nodes. For each node, the CSP uses fuzzy Integral to generate a composite value based on these criteria. The malicious node is then removed from the cloud market using this composite value. The simulation results demonstrated the potential of the proposed method to prevent nodes in the cloud market from running malware or software that can be used to degrade quality of service by exhausting resources in the cloud market.

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CryptoGA: a cryptosystem based on genetic algorithm for cloud data security

Cited by 87 publications

References 51 publications

Achieving data privacy for decision support systems in times of massive data sharing

Achieving data privacy for decision support systems in times of massive data sharing

A blockchain and smart contract-based data provenance collection and storing in cloud environment

A Novel Scheme for Malicious Nodes Detection in Cloud Markets Based on Fuzzy Logic Technique

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