AuthPDB: Authentication of Probabilistic Queries on Outsourced Uncertain Data

Bo Zhang, Boxiang Dong, Haipei Sun, Wendy Hui Wang

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

Abstract

Query processing over uncertain data has gained much attention recently. Due to the high computational complexity of query evaluation on uncertain data, the data owner can outsource her data to a server that provides query evaluation as a service. However, a dishonest server may return cheap (and incorrect) query answers, hoping that the client who has weak computational power cannot catch the incorrect results. To address the integrity issue, in this paper, we design AuthPDB, a framework that supports efficient authentication of query evaluation for both all-answer and top-k queries on outsourced probabilistic databases. Our empirical results on real-world datasets demonstrate the effectiveness and efficiency of AuthPDB.

Original languageEnglish
Title of host publicationCODASPY 2020 - Proceedings of the 10th ACM Conference on Data and Application Security and Privacy
PublisherAssociation for Computing Machinery, Inc
Pages121-132
Number of pages12
ISBN (Electronic)9781450371070
DOIs
StatePublished - 16 Mar 2020
Event10th ACM Conference on Data and Application Security and Privacy, CODASPY 2020 - New Orleans, United States
Duration: 16 Mar 202018 Mar 2020

Publication series

NameCODASPY 2020 - Proceedings of the 10th ACM Conference on Data and Application Security and Privacy

Conference

Conference10th ACM Conference on Data and Application Security and Privacy, CODASPY 2020
CountryUnited States
CityNew Orleans
Period16/03/2018/03/20

Keywords

  • data outsourcing
  • data security
  • integrity verification
  • probabilistic database

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  • Cite this

    Zhang, B., Dong, B., Sun, H., & Wang, W. H. (2020). AuthPDB: Authentication of Probabilistic Queries on Outsourced Uncertain Data. In CODASPY 2020 - Proceedings of the 10th ACM Conference on Data and Application Security and Privacy (pp. 121-132). (CODASPY 2020 - Proceedings of the 10th ACM Conference on Data and Application Security and Privacy). Association for Computing Machinery, Inc. https://doi.org/10.1145/3374664.3375731