A Privacy Preserving Cloud-Based K-NN Search Scheme with Lightweight User Loads

Yeong-Cherng Hsu,Chih-Hsin Hsueh,Ja-Ling Wu

doi:10.3390/computers9010001

Yeong-Cherng Hsu, Chih-Hsin Hsueh + Show 1 more

Open Access

https://doi.org/10.3390/computers9010001

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Abstract

With the growing popularity of cloud computing, it is convenient for data owners to outsource their data to a cloud server. By utilizing the massive storage and computational resources in cloud, data owners can also provide a platform for users to make query requests. However, due to the privacy concerns, sensitive data should be encrypted before outsourcing. In this work, a novel privacy preserving K-nearest neighbor (K-NN) search scheme over the encrypted outsourced cloud dataset is proposed. The problem is about letting the cloud server find K nearest points with respect to an encrypted query on the encrypted dataset, which was outsourced by data owners, and return the searched results to the querying user. Comparing with other existing methods, our approach leverages the resources of the cloud more by shifting most of the required computational loads, from data owners and query users, to the cloud server. In addition, there is no need for data owners to share their secret key with others. In a nutshell, in the proposed scheme, data points and user queries are encrypted attribute-wise and the entire search algorithm is performed in the encrypted domain; therefore, our approach not only preserves the data privacy and query privacy but also hides the data access pattern from the cloud server. Moreover, by using a tree structure, the proposed scheme could accomplish query requests in sub-liner time, according to our performance analysis. Finally, experimental results demonstrate the practicability and the efficiency of our method.

Highlights

By outsourcing data and/or tasks to the cloud, even devices with low computational ability can conduct analytic works with a large amount of data
We propose a privacy-preserving K-NN (PPkNN) search scheme based on the new encryption method described above, which can perform comparison and addition operations in the encrypted domain, and use an
We prove that our modified mutable order preserving encoding (mOPE) combining with Paillier cryptosystem is IND-OCPA secure based on their primitive security guarantees

Summary

Introduction

By outsourcing data and/or tasks to the cloud, even devices with low computational ability can conduct analytic works with a large amount of data. The medical records have not yet been stored on the cloud platform because the records may contain sensitive information which needs specific privacy protection before being released to public. A compromised cloud server might expose the medical dataset outsourced from medical data owners or infringe upon patients’ privacy by leaking out the associated symptoms or diagnosis results. For the protection of sensitive data, data owners usually encrypt their data before outsourcing them to the cloud. Other users who want to access the data can make query requests to the cloud. Sometimes these users want to preserve their query privacy from data owners and the cloud server.

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