Homomorphic exclusive-or operation enhance secure searching on cloud storage

Ren, Shu Qin; Tan, Benjamin Hong Meng; Sundaram, Sivaraman; Wang, Taining; Aung, Khin Mi Mi

doi:10.1109/cloudcom.2014.86

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…From Table 1, it can be observed that authors [21], [22], [23], [24], [25], [27], [28], [29], and [38] have their algorithms producing predictable, high execution and linear execution time, contrarily, Loyka et al 2018 [26], proposed an algorithm, using homomorphism scheme based on an affine cipher, which produced similar nonlinear results as the proposed Enhanced Homomorphism Scheme when text only was executed as shown in table 6, but the encryption and decryption time for numbers only was linear as shown in Table 7, whiles that of EHS is nonlinear which makes the work of Loyka et al (2018) to be defeated by the works of [34], [35], and [36] that execution time depends on the size of security key used for the execution process. From, this it can be concluded that the proposed Enhanced Homomorphism scheme's execution time is not dependent on data size but on the secret key used for the encryption as proposed by authors [34], [35], and [36].…”

Section: Encryption and Decryption Times For Data Sizes Of 𝟐 𝒏 (𝒏 ∈ 𝟐...mentioning

confidence: 96%

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Ensuring privacy and confidentiality of data on the cloud using an enhanced homomorphism scheme

Dawson

Twum²,

Acquah³

et al. 2022

IJCAI

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Cloud computing is one of the widest phenomena embraced in information technology. This result from numerous advantages associated with it making many organizations and individuals offload their data to the cloud. Encryption schemes restrict access to data from unauthorized clients, helping attain confidentiality and privacy. The modification of the ciphertext of clients' data on the cloud demand downloading, deciphering, editing, and finally uploading back to the cloud by sharing their private key with the cloud service provider making it tedious. The application of homomorphism, allows computation to be performed on ciphertext with no decipher activity which helps to avoid the surfacing of sensitive client data stored on the cloud. In this paper, an Enhanced Homomorphism Scheme (EHS) is proposed based on Good Prime Numbers (GPN), Linear Congruential Generator (LCG), Fixed Sliding Window Algorithm (FSWA), and Gentry's homomorphism scheme. A dataset from the Kaggle database was used to test the proposed algorithm. A variety of tests were conducted using the proposed algorithm such as the Uniqueness of ciphertext, addition and multiplication property of full homomorphism, and the execution times using 2 𝑛 (𝑛 ∈ 2,3,4,5) data sizes. A comparison of the execution time of the proposed EHS was conducted with the New Fully Homomorphism Scheme (NFHS), and the Enhanced Homomorphism Encryption Scheme (EHES). From the comparison, the proposed EHS algorithm had the lowest encryption time when a data size of 24kb was executed but with a higher decryption time of 567.6667 ± 96.38911when a data size of 8kb was used. On the other hand, with a data size of 32kb, EHES had the highest decryption time of 1274ms with the proposed EHS having the lowest decryption time of 551.2222 ± 82.68746 indicating a decryption percentage decrease of 56.73%. This confirms that execution times are dependent on the size of the encryption key but not on data size.Povzetek: Nov kriptografski algoritem z imenom EHS se je izkazal z izboljšanimi časi izvajanja na nekaj standardnih testnih domenah.

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Section: Encryption and Decryption Times For Data Sizes Of 𝟐 𝒏 (𝒏 ∈ 𝟐...mentioning

confidence: 96%

“…This discusses the works of other authors that are linked to ensuring data confidentiality and privacy on the cloud by employing homomorphism. The works of Ren et al (2014) and Lakhan et al [21] and [39] respectively are good examples. In the work of Ren et al, they proposed an exclusiveor (XOR) homomorphism encryption scheme.…”

Section: Related Workmentioning

confidence: 99%

Ensuring privacy and confidentiality of data on the cloud using an enhanced homomorphism scheme

Dawson

Twum²,

Acquah³

et al. 2022

IJCAI

View full text Add to dashboard Cite

show abstract

“…Encryption [6,7,8,9] Homomorphic Encryption [7] All types of queries Strong -Impractical for real-life applications due to prohibitive computation cost.…”

Section: Supported Queries Privacy Strength Limitationsmentioning

confidence: 99%

“…• Partial Homomorphic Encryption: Also known as Somewhat Homomorphic Encryption (SHE) [8], it allows the cloud to perform only a limited number of operations on encrypted data, leading to improved performance. Even though SHE schemes are less powerful than FHE schemes, they can already be used in many real-world applications in the medical, financial, and advertising domains.…”

Section: Emerging Solutionsmentioning

confidence: 99%