A locality sensitive hashing based approach for federated recommender system

Hongsheng Hu, Gillian Dobbie, Zoran Salcic, Meng Liu, Jianbing Zhang, Xuyun Zhang*

*Corresponding author for this work

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

Abstract

The recommender system is an important application in big data analytics because accurate recommendation items or high-valued suggestions can bring high profit to both commercial companies and customers. To make precise recommendations, a recommender system often needs large and fine-grained data for training. In the current big data era, data often exist in the form of isolated islands, and it is difficult to integrate the data scattered due to privacy security concerns. Moreover, privacy laws and regulations make it harder to share data. Therefore, designing a privacy-preserving recommender system is of paramount importance. Existing privacy-preserving recommender system models mainly adapt cryptography approaches to achieve privacy preservation. However, cryptography approaches have heavy overhead when performing encryption and decryption operations and they lack a good level of flexibility. In this paper, we propose a Locality Sensitive Hashing (LSH) based approach for federated recommender system. Our proposed efficient and scalable federated recommender system can make full use of multiple source data from different data owners while guaranteeing preservation of privacy of contributing parties. Extensive experiments on real-world benchmark datasets show that our approach can achieve both high time efficiency and accuracy under small privacy budgets.

Original languageEnglish
Title of host publicationProceedings - 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGRID 2020
EditorsLaurent Lefevre, Carlos A. Varela, George Pallis, Adel N. Toosi, Omer Rana, Rajkumar Buyya
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages836-842
Number of pages7
ISBN (Electronic)9781728160955
DOIs
Publication statusPublished - 2020
Event20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGRID 2020 - Melbourne, Australia
Duration: 11 May 202014 May 2020

Publication series

NameProceedings - 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGRID 2020

Conference

Conference20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGRID 2020
CountryAustralia
CityMelbourne
Period11/05/2014/05/20

Keywords

  • recommender system
  • locality sensitive hashing
  • differential privacy

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