On the comparison of classifiers' construction over private inputs

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Classifiers are often trained over data collected from different sources. Sharing their data with other entities, however, can raise privacy concerns for data owners. To protect data confidentiality while being able to train a classifier, effective solutions have been proposed in the literature to construct various types of classifiers over private data. However, to date an analysis and comparison of the computation and communication costs for the construction of classifiers over private data is missing, making it difficult to determine which classifier can be used in a given application domain. In this work, we show how two well-known classifiers (Naive Bayes and SVM classifiers) can be securely build over private inputs, and evaluate their construction costs. We assess the computation and communication costs for training the classifiers both theoretically and empirically for different benchmark datasets.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2020
EditorsGuojun Wang, Ryan Ko, Md Zakirul Alam Bhuiyan, Yi Pan
PublisherInstitute of Electrical and Electronics Engineers
Pages691-698
Number of pages8
ISBN (Electronic)9780738143804
DOIs
Publication statusPublished - Dec 2020
Event19th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2020 - Guangzhou, China
Duration: 29 Dec 20201 Jan 2021

Conference

Conference19th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2020
CountryChina
CityGuangzhou
Period29/12/201/01/21

Keywords

  • ABY library
  • Classification
  • Comparison
  • Privacy
  • Secure two-party computation

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