Scholarlink

<p>WiFi fingerprint-based localization technology is essential in the Internet of Things (IoT) and is widely used in mobile applications such as indoor navigation and tracking. However, this technique suffers from privacy issues that may leak the user’s location and location server (LS) data. Rich localization resources and increasingly complex algorithms have gradually made cloud environments the mainstream of indoor positioning services. Although cloud environments improve service quality with their rich resources and high concurrent processing capabilities, they also bring higher privacy risks. Current privacy-preserving schemes, which are primarily developed for the traditional two-entity localization model, fail to adequately address the requirements of three-entity indoor positioning services in cloud environments and often involve substantial computational and communication overhead. To address these issues, this paper proposes a new privacy-preserving localization scheme employing Oblivious Transfer and Locality Sensitive Hashing called PriOT-LSH, aiming to protect the data privacy of both clients and service providers and provide efficient location services. Users can securely and quickly query their closest K location-related information from the server via high-dimensional WiFi fingerprint information without revealing specific choices. Theoretical analysis and experimental results show that the PriOT-LSH scheme maintains its efficiency in handling large-scale datasets without significantly increasing overhead with database size. Compared with existing schemes, PriOT-LSH shows significant advantages in both localization accuracy and efficiency, with a 6.71–13.59% improvement in localization accuracy compared to existing schemes, and each online query takes no more than 1&#xa0;s with low computational and communication overheads.</p>

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

PriOT-LSH: efficient privacy-preserving WiFi localization based on oblivious transfer and locality-sensitive hashing in cloud environments

  • 期刊论文

  • 发表日期：

    2025年9月11日

查看全文
  • Fenhua Bai,
  • Tongyu Pu,
  • Tao Shen,
  • Chi Zhang,
  • Xiaohui Zhang

摘要

WiFi fingerprint-based localization technology is essential in the Internet of Things (IoT) and is widely used in mobile applications such as indoor navigation and tracking. However, this technique suffers from privacy issues that may leak the user’s location and location server (LS) data. Rich localization resources and increasingly complex algorithms have gradually made cloud environments the mainstream of indoor positioning services. Although cloud environments improve service quality with their rich resources and high concurrent processing capabilities, they also bring higher privacy risks. Current privacy-preserving schemes, which are primarily developed for the traditional two-entity localization model, fail to adequately address the requirements of three-entity indoor positioning services in cloud environments and often involve substantial computational and communication overhead. To address these issues, this paper proposes a new privacy-preserving localization scheme employing Oblivious Transfer and Locality Sensitive Hashing called PriOT-LSH, aiming to protect the data privacy of both clients and service providers and provide efficient location services. Users can securely and quickly query their closest K location-related information from the server via high-dimensional WiFi fingerprint information without revealing specific choices. Theoretical analysis and experimental results show that the PriOT-LSH scheme maintains its efficiency in handling large-scale datasets without significantly increasing overhead with database size. Compared with existing schemes, PriOT-LSH shows significant advantages in both localization accuracy and efficiency, with a 6.71–13.59% improvement in localization accuracy compared to existing schemes, and each online query takes no more than 1 s with low computational and communication overheads.