Safety kNN query method based on LBS

Abstract

The present invention belongs to the field of data privacy protection, and provides a safety kNN query method based on the LBS. The flows of the method comprise the steps of: generating a key pair andan encryption index structure by a data owner, sending the encryption index structure to a server C1, sending a public key to the server C1, a server C2 and a user, and sending a private key to the server C2; employing public key encryption to generate an encryption query request for the query of the user itself, and sending the query request to the server C1; after the server C1 obtains the encryption index structure and an encrypted query request, defining secure two party computation; based on the secure two party computation, designing a safety kNN query protocol; and returning a query result to a user. The safety kNN query method based on the LBS can effectively protect the privacy of data on the server, the privacy of the query request of the user, the privacy of the query result ofthe user and the access mode in the query process, and provides an accurate query result, is suitable for a mobile device with a lower processing capacity and greatly improves the speed completing the query.

Description

technical field [0001] The invention belongs to the field of data privacy protection, and in particular relates to a secure kNN query method based on LBS. Background technique [0002] In recent years, with the rapid development of mobile networks and smart phones, location-based services (LBS) have been widely used in various aspects such as social networking, life services, and online shopping. Among them, kNN query, that is, querying the k nearest neighbor points closest to the user's position, is a basic and representative important query. However, while LBS brings great convenience to our life, it also brings us the hidden danger of privacy leakage. From the perspective of user privacy, when performing a location-based query, the server can easily collect the user's real location while responding to the user's request, and infer private information such as religious belief, home address, and daily life trajectory from it. From the perspective of enterprises, many ente...

AI Technical Summary

Problems solved by technology

However, the intermediate results of encrypted calculations can still reveal the data access mode, that is, which data is accessed, which is often vulnerable to reasoning attacks

At the same time, in this process, the server needs to calculate on the ciphertext to obtain the query result. However, due to the complexity of the ciphertext calculation, the current proposed method often needs to consume a large time cost, which makes the security of the query service It is difficult to balance with the query cost

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