Enabling secure and efficient kNN query processing over encrypted spatial data in the cloud

Cheng, Xiang; Sen, Sandip; Teng, Yiping; Xiao, Ke

doi:10.1002/sec.1245

Cited by 7 publications

(4 citation statements)

References 20 publications

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“…However, this scheme leaks information to an attacker knowing some of the points in S [23], in addition some data leaks to the management system. In [7], Cheng et al proposed a similar faster scheme where their speedup was achieved by using a secure index. Their scheme relies upon a cloud server that does not collude with the data owner, a model which is not always possible or trusted.…”

Section: Related Workmentioning

confidence: 99%

Secure $k$-ish Nearest Neighbors Classifier

Shaul¹,

Feldman²,

Rus³

2018

Preprint

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In machine learning, classifiers are used to predict a class of a given query based on an existing (already classified) database. Given a database S of n d-dimensional points and a d-dimensional query q, the k-nearest neighbors (kNN) classifier assigns q with the majority class of its k nearest neighbors in S.In the secure version of kNN, S and q are owned by two different parties that do not want to share their (sensitive) data. A secure classifier has many applications. For example, diagnosing a tumor as malignant or benign. Unfortunately, all known solutions for secure kNN either require a large communication complexity between the parties, or are very inefficient to run (estimated running time of weeks). In this work we present a classifier based on kNN, that can be implemented efficiently with homomorphic encryption (HE). The efficiency of our classifier comes from a relaxation we make on kNN, where we allow it to consider κ nearest neighbors for κ ≈ k with some probability. We therefore call our classifier k-ish Nearest Neighbors (k-ish NN). The success probability of our solution depends on the distribution of the distances from q to S and increase as its statistical distance to Gaussian decrease. To efficiently implement our classifier we introduce the concept of doublyblinded coin-toss. In a doubly-blinded coin-toss the success probability as well as the output of the toss are encrypted. We use this coin-toss to efficiently approximate the average and variance of the distances from q to S. We believe these two techniques may be of independent interest. When implemented with HE, the k-ish NN has a circuit depth (or polynomial degree) that is independent of n, therefore making it scalable. We also implemented our classifier in an open source library based on HELib [13] and tested it on a breast tumor database. The accuracy of our classifier (measured as F1 score) were 98% and classification took less than 3 hours compared to (estimated) weeks in current HE implementations.

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Section: Related Workmentioning

confidence: 99%

Secure $k$-ish Nearest Neighbors Classifier

Shaul¹,

Feldman²,

Rus³

2018

Preprint

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“… Hu et al (2011) proposed a secure traversal framework that can used, together with privacy homomorphism, to achieve secure k -NN query processing protocol. Cheng et al (2015) proposed a privacy-preserving protocol that employs an encrypted hierarchical index tree to perform k -NN queries over spatial data outsourced to cloud in encrypted form. All three protocols ( Hu et al, 2011 ; Zhu, Huang & Takagi, 2016 ; Cheng et al, 2015 ) leak data access pattern to the cloud.…”

Section: Introductionmentioning

confidence: 99%

“… Cheng et al (2015) proposed a privacy-preserving protocol that employs an encrypted hierarchical index tree to perform k -NN queries over spatial data outsourced to cloud in encrypted form. All three protocols ( Hu et al, 2011 ; Zhu, Huang & Takagi, 2016 ; Cheng et al, 2015 ) leak data access pattern to the cloud. On the other hand, Kesarwani et al (2018) proposed a secure k -NN query processing protocol over encrypted data by utilizing a leveled fully homomorphic encryption scheme.…”

Section: Introductionmentioning

confidence: 99%

Privacy-preserving k-NN interpolation over two encrypted databases

Osmanoglu¹,

Demir²,

Tuğrul³

2022

PeerJ Computer Science

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Cloud computing enables users to outsource their databases and the computing functionalities to a cloud service provider to avoid the cost of maintaining a private storage and computational requirements. It also provides universal access to data, applications, and services without location dependency. While cloud computing provides many benefits, it possesses a number of security and privacy concerns. Outsourcing data to a cloud service provider in encrypted form may help to overcome these concerns. However, dealing with the encrypted data makes it difficult for the cloud service providers to perform some operations over the data that will especially be required in query processing tasks. Among the techniques employed in query processing task, the k-nearest neighbor method draws attention due to its simplicity and efficiency, particularly on massive data sets. A number of k-nearest neighbor algorithms for query processing task on a single encrypted database have been proposed. However, the performance of k-nearest neighbor algorithms on a single database may create accuracy and reliability problems. It is a fact that collaboration among different cloud service providers yields more accurate and more reliable results in query processing. By considering this fact, we focus on the k-nearest neighbor (k-NN) problem over two encrypted databases. We introduce a secure two-party k-NN interpolation protocol that enables a query owner to extract the interpolation of the k-nearest neighbors of a query point from two different databases outsourced to two different cloud service providers. We also show that our protocol protects the confidentiality of the data and the query point, and hides data access patterns. Furthermore, we conducted a number of experiment to demonstrate the efficiency of our protocol. The results show that the running time of our protocol is linearly dependent on both the number of nearest neighbours and data size.

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“…Searchable encryption (SE) is a hot topic in cryptography that seeks protocols permitting efficient search on outsourced encrypted data. While most solutions focus on the basic query types such as exact match and range queries, in recent years, there is a rise of interest in SE schemes for kNN queries, e.g., [5,9,23,29,30]. However, there are still no solutions for kNN or AkNN search providing strong security guarantees.…”

Section: Introductionmentioning

confidence: 99%

Privacy-Preserving Approximate k-Nearest-Neighbors Search that Hides Access, Query and Volume Patterns

Boldyreva

Tang

2021

Proceedings on Privacy Enhancing Technologies

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We study the problem of privacy-preserving approximate kNN search in an outsourced environment — the client sends the encrypted data to an untrusted server and later can perform secure approximate kNN search and updates. We design a security model and propose a generic construction based on locality-sensitive hashing, symmetric encryption, and an oblivious map. The construction provides very strong security guarantees, not only hiding the information about the data, but also the access, query, and volume patterns. We implement, evaluate efficiency, and compare the performance of two concrete schemes based on an oblivious AVL tree and an oblivious BSkiplist.

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Enabling secure and efficient kNN query processing over encrypted spatial data in the cloud

Cited by 7 publications

References 20 publications

Secure $k$-ish Nearest Neighbors Classifier

Secure $k$-ish Nearest Neighbors Classifier

Privacy-preserving k-NN interpolation over two encrypted databases

Privacy-Preserving Approximate k-Nearest-Neighbors Search that Hides Access, Query and Volume Patterns

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