Efran (O): "Efficient Scalar Homomorphic Scheme on MapReduce for Data Privacy Preserving"

Konan, Martin; Wang, Wenyong; Agyemang, Brighter

doi:10.1109/cscloud.2016.10

Cited by 5 publications

(7 citation statements)

References 17 publications

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“…As discussed in [17] the related work [16] requires additional computation at the reduce stage, the DGHV [12] and Gen 10 [13] schemes send respectively their private key and sensitive security parameters to unreliable public cloud server (compromising the cryptosystem). Additionally, the above mentioned models don't address the security and efficiency ciphertext retrieval issues.…”

Section: Fhe_shcr Scheme In [17]mentioning

confidence: 99%

“…Additionally, the above mentioned models don't address the security and efficiency ciphertext retrieval issues. Therefore, authors in [17] present their contribution FHE_SHCR, which is based on schemes [13] [18] and [19] to address the privacy shortcomings of models [12] [13] and [16]. Subsequently, the main objective of this model is to securely retrieve ciphertexts at reduce stage and enhance the retrieval algorithm accuracy without getting any information about the content of intermediate searchable ciphertext to fix the security shortcomings in [12] and [13].…”

Section: Fhe_shcr Scheme In [17]mentioning

confidence: 99%

“…Subsequently, the main objective of this model is to securely retrieve ciphertexts at reduce stage and enhance the retrieval algorithm accuracy without getting any information about the content of intermediate searchable ciphertext to fix the security shortcomings in [12] and [13]. The FHE_SCHR scheme is an efficient candidate for homomorphic encryption to preserve data privacy in cloud by a strong hybrid encryption [17].…”

Section: Fhe_shcr Scheme In [17]mentioning

confidence: 99%

“…Recall that this paper is based on [17], and therefore we use the same cryptosystem to fix the data privacy preserving challenge. Furthermore, to enhance the security of our proposed solution in terms of data authentication paths and dynamics, we adopt the Merkle's hash tree to store the metadata decomposition…”

Section: Fhe_schr Security Analysismentioning

confidence: 99%

“…To summarize the security analysis, we can say by implementing a secure front end database management agent (Anonymiser) on top of FHE_SHCR security mechanism [17] that the data privacy insurance has been greatly reinforced in our proposed solution (Optimized FHE_SHCR).…”

Section: ( )mentioning

confidence: 99%

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Optimized Homomorphic Scheme on Map Reduce for Data Privacy Preserving

Konan¹,

Wang²,

Agyemang³

2017

JIS

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Security insurance is a paramount cloud services issue in the most recent decade. Therefore, Mapreduce which is a programming framework for preparing and creating huge data collections should be optimized and securely implemented. But, conventional operations on ciphertexts were not relevant. So there is a foremost need to enable particular sorts of calculations to be done on encrypted data and additionally optimize data processing at the Map stage. Thereby schemes like (DGHV) and (Gen 10) are presented to address data privacy issue. However private encryption key (DGHV) or key's parameters (Gen 10) are sent to untrusted cloud server which compromise the information security insurance. Therefore, in this paper we propose an optimized homomorphic scheme (Op_FHE_SHCR) which speed up ciphertext (c R) retrieval and addresses metadata dynamics and authentication through our secure Anonymiser agent. Additionally for the efficiency of our proposed scheme regarding computation cost and security investigation, we utilize a scalar homomorphic approach instead of applying a blinding probabilistic and polynomial-time calculation which is computationally expensive. Doing as such, we apply an optimized ternary search tries (TST) algorithm in our metadata repository which utilizes Merkle hash tree structure to manage metadata authentication and dynamics.

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Section: Fhe_shcr Scheme In [17]mentioning

confidence: 99%