Scholarlink

In mobile communication, spatial queries pose a significant threat to user location privacy, as the location of a query can reveal sensitive information about the mobile user. This chapter focuses on approximate k nearest neighbor (kNN) queries, where a mobile user queries a location-based service (LBS) provider to retrieve the approximate k nearest points of interest (POIs) based on their current location. We introduce two solutions: a basic solution and a generic solution, both aimed at preserving the user’s location and query privacy in approximate kNN queries. The proposed solutions leverage the Paillier public-key cryptosystem and ensure both location and query privacy. To safeguard query privacy, the basic solution allows the mobile user to retrieve one type of POI, such as approximate k nearest car parks, without revealing the type of POI being queried to the LBS provider. The generic solution, on the other hand, extends this capability to support multiple discrete attributes in private location-based queries. Compared to existing methods for kNN queries with location privacy, our solutions demonstrate higher efficiency.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Private k Nearest Neighbor Queries with Location Privacy

  • 章节

  • 发表日期：

    2025年

查看全文
  • Xun Yi,
  • Xuechao Yang,
  • Xiaoning Liu,
  • Andrei Kelarev,
  • Kwok-Yan Lam,
  • Mengmeng Yang,
  • Xiangning Wang,
  • Elisa Bertino

摘要

In mobile communication, spatial queries pose a significant threat to user location privacy, as the location of a query can reveal sensitive information about the mobile user. This chapter focuses on approximate k nearest neighbor (kNN) queries, where a mobile user queries a location-based service (LBS) provider to retrieve the approximate k nearest points of interest (POIs) based on their current location. We introduce two solutions: a basic solution and a generic solution, both aimed at preserving the user’s location and query privacy in approximate kNN queries. The proposed solutions leverage the Paillier public-key cryptosystem and ensure both location and query privacy. To safeguard query privacy, the basic solution allows the mobile user to retrieve one type of POI, such as approximate k nearest car parks, without revealing the type of POI being queried to the LBS provider. The generic solution, on the other hand, extends this capability to support multiple discrete attributes in private location-based queries. Compared to existing methods for kNN queries with location privacy, our solutions demonstrate higher efficiency.