Secure kNN computation on encrypted databases

By W. Wong, David Wai-Lok Cheung, B. Kao, Nikos Mamoulis. SIGMOD Conference, 2009

985 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/8653934b00dfb802a7dd066f8f52c5dd3b267831

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Kui Ren
2. Wenjing Lou
3. Ming Li
4. Ning Cao
5. Xingming Sun
6. Shucheng Yu
7. Xinhui Wang
8. Zhihua Xia

In Computational Theory and Mathematics

1. Kui Ren
2. Wenjing Lou
3. Shucheng Yu
4. Ning Cao
5. Bing Wang
6. Li Xiong
7. Xinhui Wang
8. Yonghui Xiao

Citations in the corpus

• Surf feature extraction in encrypted domain (2014, methodology): “Wong et al [4] presented a method for identification of a key word in the encrypted document.”
• Privacy-preserving multi-keyword ranked search over encrypted cloud data (2011, methodology): “1) Secure kNN Computation: In the secure k-nearest neighbor (kNN) scheme [28], Euclidean distance between a database record pi and a query vector q is used to select k nearest database records.”
• Privacy-preserving multi-keyword fuzzy search over encrypted data in the cloud (2014, methodology): “In terms of level of privacy protection, we consider two threat models depending on the information available to the cloud server, which are also used in other related works [8], [17].”
• Secure k-nearest neighbor query over encrypted data in outsourced environments (2013, methodology): “• Low computation overhead on Bob - After sending his encrypted query record to the cloud, Bob in volves only in a little computation compared with the existing work s [13,28,31].”
• Protecting Locations with Differential Privacy under Temporal Correlations (2014, background): “PIR based meth-ods (e.g. [22, 41, 44, 55]) guarantee cryptographic privacy by allowing data retrieval from a database without revealing any information to the database server about the retrieved item.”
• A Privacy-Preserving and Copy-Deterrence Content-Based Image Retrieval Scheme in Cloud Computing (2016, methodology): “In our scheme, the feature vectors are encrypted by the secure kNN algorithm which is proved to be secure against the Chipertext-only Attack (COA) model [37].”
• Privacy-Preserving Multi-Keyword Ranked Search over Encrypted Cloud Data (2014, methodology): “In the secure kNN scheme [27], euclidean distance between a data record pi and a query vector q is used to select k nearest database records.”
• A Secure and Dynamic Multi-Keyword Ranked Search Scheme over Encrypted Cloud Data (2016, methodology): “According to [38], the attacker (cloud server) of COA cannot calculate the matrices merely with ciphertext.”
• Enabling Personalized Search over Encrypted Outsourced Data with Efficiency Improvement (2016, methodology): “The security of this scheme depends upon secure kNN [22] which mainly contains three steps: matrix encryption, random split and dimension extension.”