Confidentiality-preserving based on attribute encryption using auditable access during encrypted records in cloud location

Raja, J.; Ramakrishnan, M.

doi:10.1007/s11227-019-03111-1

Cited by 6 publications

(7 citation statements)

References 15 publications

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“…However, the drawback of Elmehdwi et al's algorithm is excessively high computational cost because SBD, SMIN, and SMIN n protocols use an encrypted binary array as input value. For example, when we perform the SMIN protocol between E(8) and E (7), clouds transform an encrypted decimal value into an encrypted binary array: E(8) (10) = {E(1), E(0), E(0), E(0)} (2) , E(7) (10) = {E(0), E(1), E(1), E(1)} (2) . After that, clouds perform the SMIN protocol between E(8) (10) = {E(1), E(0), E(0), E(0)} (2) and E(7) (10) = {E(0), E(1), E(1), E(1)} (2) .…”

Section: Discussionmentioning

confidence: 99%

“…For example, when we perform the SMIN protocol between E(8) and E (7), clouds transform an encrypted decimal value into an encrypted binary array: E(8) (10) = {E(1), E(0), E(0), E(0)} (2) , E(7) (10) = {E(0), E(1), E(1), E(1)} (2) . After that, clouds perform the SMIN protocol between E(8) (10) = {E(1), E(0), E(0), E(0)} (2) and E(7) (10) = {E(0), E(1), E(1), E(1)} (2) . As a result, the SMIN requires high computational cost because it performs the encrypted operations as many times as the length of data domain in bit.…”

Section: Discussionmentioning

confidence: 99%

“…In Fig. 3, C A performs ESSED(E(d 5 ), < E( 6),E(1) >) for E(d 5 ) and E(q), and stores the ESSED result, i.e., E (2), into E(dist 5 ). Second, the minimum value is calculated by using the SMIN n and stored the SMIN n result into E(dist min )(②).…”

Section: Knn Retrieval Phasementioning

confidence: 99%

“…In Fig. 3, C A performs SMIN n (E(2), E( 9), E( 8), E( 18)) for obtaining the minimum distance among E(dist 5 ), E(dist 6 ), E(dist 7 ), and E(dist 8 ), and stores the SMIN n result, i.e., E (2), into E(dist min ). Third, in order to obtain the encrypted data item with the minimum distance to the given query, C A performs E(dist min − dist i ) and stores the result into E( i ) for 1 ≤ i ≤ cnt (③).…”

Section: Knn Retrieval Phasementioning

confidence: 99%

“…Research on preserving data privacy in outsourced databases has been spotlighted with the development of a cloud computing. Since a data owner (DO) outsources his/her databases and allows a cloud to manage them, the DO can reduce the database management cost by using the cloud's resources with flexibility [1][2][3]. The cloud not only maintains the databases, but also provides an authorized user (AU) with querying services on the outsourced databases.…”

Section: Introductionmentioning

confidence: 99%

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Privacy-preserving kNN query processing algorithms via secure two-party computation over encrypted database in cloud computing

Kim

Lee

Kim³

et al. 2022

J Supercomput

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Since studies on privacy-preserving database outsourcing have been spotlighted in a cloud computing, databases need to be encrypted before being outsourced to the cloud. Therefore, a couple of privacy-preserving kNN query processing algorithms have been proposed over the encrypted database. However, the existing algorithms are either insecure or inefficient. Therefore, in this paper we propose a privacy-preserving kNN query processing algorithm via secure two-party computation on the encrypted database. Our algorithm preserves both data privacy and query privacy while hiding data access patterns. For this, we propose efficient and secure protocols based on Yao’s garbled circuit. To achieve a high degree of efficiency in query processing, we also propose a parallel kNN query processing algorithm using encrypted random value pool. Through our performance analysis, we verify that our proposed algorithms outperform the existing ones in terms of a query processing cost.

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