DAG: a general model for privacy-preserving data mining (extended abstract)

Sin G. Teo, Jianneng Cao, Vincent C.S. Lee

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

Abstract

Secure multi-party computation (SMC) allows parties to jointly compute a function over their inputs, while keeping every input confidential. SMC has been extensively applied in tasks with privacy requirements, such as privacy-preserving data mining (PPDM), to learn task output and at the same time protect input data privacy. However, existing SMC-based solutions are ad-hoc - they are proposed for specific applications, and thus cannot be applied to other applications directly. To address this issue, we propose a privacy model DAG (Directed Acyclic Graph) that consists of a set of fundamental secure operators (e.g., +, -, ×, /, and power). Our model is general - its operators, if pipelined together, can implement various functions, even complicated ones. The experimental results also show that our DAG model can run in acceptable time.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE 36th International Conference on Data Engineering, ICDE 2020
EditorsMurat Kantarcioglu, Dimitrios Gunopulos, S. Sudarshan
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages2018-2019
Number of pages2
ISBN (Electronic)9781728129037
ISBN (Print)9781728129044
DOIs
Publication statusPublished - 2020
EventIEEE International Conference on Data Engineering 2020 - Online, Virtual, Dallas, United States of America
Duration: 20 Apr 202024 Apr 2020
Conference number: 36th
https://ieeexplore.ieee.org/xpl/conhome/9093725/proceeding (Proceedings)
https://www.utdallas.edu/icde/ (Website)

Publication series

NameProceedings - International Conference on Data Engineering
PublisherThe Institute of Electrical and Electronics Engineers, Inc.
Volume2020-April
ISSN (Print)1084-4627
ISSN (Electronic)2375-026X

Conference

ConferenceIEEE International Conference on Data Engineering 2020
Abbreviated titleICDE 2020
CountryUnited States of America
CityDallas
Period20/04/2024/04/20
Internet address

Keywords

  • And Data Mining
  • DAG
  • Operator
  • Privacy
  • Secure

Cite this