Efficient privacy-preserving frequent itemset query over semantically secure encrypted cloud database

Wu, Wei; Xian, Ming; Parampalli, Udaya; Lü, Bin

doi:10.1007/s11280-021-00863-w

Cited by 6 publications

(8 citation statements)

References 47 publications

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“…Hesaplama hizmetinin dış kaynaklarla gerçekleştirildiği biyometrik doğrulama sistemlerinde gizliliğin korunmasını hedefleyen çalışmada [40] sunucu tarafında işlenen verilerin istemci ortamından çıkmadan şifrelenerek korunması yaklaşımı izlenmiş ve homomorfik şifreleme tekniğinden faydalanılmıştır. Bulut servis sağlayıcıları tarafından sunulan veri madenciliğ i hizmetinde gizliliği korunmak amacıyla gerçekleştirilen çalışmada [41], hem etkin bir güvenlik sunan hem de düşük hesaplama gücü ihtiyacı bulunan, homomorfik şifreleme ve şifreli metin paketleme tekniğinin kullanıldığı gizlilik korumalı sık öğe seti sorgusu (PPFIQ) şeması sunulmuştur.…”

Section: Gizlilik (Privacy)unclassified

A Review on Challenges in Cloud Computing Security and Recent Researchs

GÜLBURUN

Dener

2022

Bilişim Teknolojileri Dergisi

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Bulut bilişim sistemleri, kişi ve organizasyonlar tarafından ihtiyaç duyulan farklı seviye ve nitelikteki bilg i sistemleri kaynaklarının, talebe bağlı olarak istenilen zaman ve miktarda ihtiyaç sahibine sunulabildiği, çoğunlukla internet üzerinden erişilebilir kılınan, özellikle değişken iş yüklerine sahip organizasyonların ihtiyaçlarına en iyi şekilde cevap verebilen sistemlerdir. Bulut bilişim sistemlerinin kullanımının artmasına paralel olarak hem bulut biliş im platformlarına hem de bu platformlardan servis alan organizasyonlara yönelik saldırılar artış göstermiştir. Çalışmad a, öncelikle, farklı kuruluşlar tarafından hazırlanan bulut bilişim güvenlik raporları incelenerek, bulut bilişim kapsamında karşılaşılan temel tehditler ortaya konmuştur. Daha sonra Web of Science veri tabanı temel alınarak bulut biliş im güvenliği kapsamında yapılan güncel çalışmalar incelenmiştir.

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Section: Gizlilik (Privacy)unclassified

A Review on Challenges in Cloud Computing Security and Recent Researchs

GÜLBURUN

Dener

2022

Bilişim Teknolojileri Dergisi

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“…Fifthly, C A obtains NG * by shuffling the ids of nodes using π-1 in each NG′ (line 12-13). Finally, C A accesses one data in each node for each NG * and performs E(t' i,j ) = SMR(E(node z .data s,j ), E(α z )) where 1 ≤ s ≤ FanOut and 1 ≤ j ≤ m (line [14][15][16][17][18][19][20][21][22]. Here, E(α z ) is the result of GSPE corresponding to node z .…”

Section: Candidate Node Search Phasementioning

confidence: 99%

“…Fifthly, C A obtains E(V) by shuffling E(U) using π-1. Then, C A performs SMR protocol by using E(V i ) and E(cand i,j ) to obtain E(Vʹ i,j ) (lines [14][15][16][17]. Sixthly, by computing E(nn s,j ) = cnt ∏ i=1 E(V � i,j ) for 1 ≤ j ≤ m, C A can securely extract the datum corresponding to the E(dist min ) (line 18).…”

Section: Knn Retrieval Phasementioning

confidence: 99%

“…Therefore, a vital challenge in the cloud computing is to protect both data privacy and query privacy among the data owner, the users, and the cloud. However, during query processing, the cloud can derive sensitive information about the actual data items and users by observing data access patterns even if the data and the query are encrypted [11][12][13][14][15][16][17]. In addition, it is very challenging to process a query on the encrypted data without having to decrypt it.…”

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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“…The second threats to validity is related to the performance of the proposed method compared to other more recent methods. Even though N V is not considered as the recent one, however, recent researches in PPDM [34], [35] still consider the method as the benchmark to evaluate the performance of their proposed method. Therefore, the impact of using other recent methods is small.…”

Section: A Evaluation Metricsmentioning

confidence: 99%

Swapping-based Data Sanitization Method for Hiding Sensitive Frequent Itemset in Transaction Database

Gunawan¹,

Nugroho²,

Maryam³

2021

IJACSA

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Sharing a transaction database with other parties for exploring valuable information becomes more recognized by business institutions, i.e., retails and supermarkets. It offers various benefits for the institutions, such as finding customer shopping behavior and frequently bought items, known as frequent itemsets. Due to the importance of such information, some institutions may consider certain frequent itemsets as sensitive information that should be kept private. Therefore, prior to handling a database, the institutions should consider privacy preserving data mining (PPDM) techniques for preventing sensitive information breaches. Presently, several PPDM methods, such as item suppression-based methods and item insertionbased methods have been developed. Unfortunately, the methods result in significant changes to the database and induce some side effects such as hiding failure, significant data dissimilarity, misses cost, and artificial frequent itemset occurrence. In this paper, we propose a swapping-based data sanitization method that can hide the sensitive frequent itemset while at the same time it can minimize the side effects of the data sanitization process. Experimental results indicate that the proposed method outperforms existing methods in terms of minimizing the side effects.

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Efficient privacy-preserving frequent itemset query over semantically secure encrypted cloud database

Cited by 6 publications

References 47 publications

A Review on Challenges in Cloud Computing Security and Recent Researchs

A Review on Challenges in Cloud Computing Security and Recent Researchs

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

Swapping-based Data Sanitization Method for Hiding Sensitive Frequent Itemset in Transaction Database

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