Privacy-preserving Recommendation for Location-based Services

Qiuyi Lyu, Yu Ishimaki, Hayato Yamana

研究成果: Conference contribution

4 被引用数 (Scopus)


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.

ホスト出版物のタイトル2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019
出版社Institute of Electrical and Electronics Engineers Inc.
出版ステータスPublished - 2019 5月 10
イベント4th IEEE International Conference on Big Data Analytics, ICBDA 2019 - Suzhou, China
継続期間: 2019 3月 152019 3月 18


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


Conference4th IEEE International Conference on Big Data Analytics, ICBDA 2019

ASJC Scopus subject areas

  • 人工知能
  • 情報システム
  • 情報システムおよび情報管理
  • 統計学、確率および不確実性


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