Privacy-preserving Recommendation for Location-based Services

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Location-based recommendation services, such as Foursquare, enhance the convenience in the life of consumers. Users are usually sensitive to disclose their personal information. Unavoidable security concerns arise because malicious third parties could misuse confidential information, such as the users' preferences. The mainstream research to this problem is employing the privacy-preserving k-NN search algorithm. However, two major bottlenecks exist. One is that it only provides the nearest points of interest (POI) to the users without any recommendations based on the users' behavior history. This limited service eventually results in a situation in which no user would prefer to continue using it. The other is that only a single user holds the private key; thus, the service providers cannot obtain any user's information to analyze to make a profit. To solve the first problem, our proposed protocol provides recommendation services by adopting collaborative filtering techniques with an encrypted database based on fully homomorphic encryption aside from encrypting both the user's location and preferences. For the second problem, a privacy service provider (PSP) is designed to generate and hold the private key. Thus, service providers can homomorphically compute aggregate information concerning user behavior patterns and send the encrypted results to PSP to ensure decryption while maintaining the privacy of individual users. Compared with the previous studies, the novelty of the proposed protocol is the design of a commercially valuable privacy recommendation mechanism that could benefit both consumers and service providers on LBS.

Original languageEnglish
Title of host publication2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages98-105
Number of pages8
ISBN (Electronic)9781728112824
DOIs
Publication statusPublished - 2019 May 10
Event4th IEEE International Conference on Big Data Analytics, ICBDA 2019 - Suzhou, China
Duration: 2019 Mar 152019 Mar 18

Publication series

Name2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019

Conference

Conference4th IEEE International Conference on Big Data Analytics, ICBDA 2019
CountryChina
CitySuzhou
Period19/3/1519/3/18

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Keywords

  • LBS
  • location-based service
  • PPDM
  • privacy-preserving data mining

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems
  • Information Systems and Management
  • Statistics, Probability and Uncertainty

Cite this

Lyu, Q., Ishimaki, Y., & Yamana, H. (2019). Privacy-preserving Recommendation for Location-based Services. In 2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019 (pp. 98-105). [8713189] (2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICBDA.2019.8713189