Obscure: Information-Theoretically Secure, Oblivious, and Verifiable Aggregation Queries

Peeyush Gupta, Yin Li, Sharad Mehrotra, Nisha Panwar, Shantanu Sharma

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

Abstract

We develop a secret-sharing-based prototype, entitled Obscure that provides communication-efficient and information-theoretically secure algorithms for aggregation queries using multi-party computation (MPC). The query execution algorithms over secret-shared data are developed to deal with an honest but curious, as well as, a malicious server by providing result verification algorithms. Obscure prevents an adversary to know the data, the query, and the tuple-identity satisfying the query.

Original languageEnglish (US)
Title of host publicationCODASPY 2020 - Proceedings of the 10th ACM Conference on Data and Application Security and Privacy
PublisherAssociation for Computing Machinery, Inc
Pages165-167
Number of pages3
ISBN (Electronic)9781450371070
DOIs
StatePublished - Mar 16 2020
Event10th ACM Conference on Data and Application Security and Privacy, CODASPY 2020 - New Orleans, United States
Duration: Mar 16 2020Mar 18 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
Period3/16/203/18/20

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Keywords

  • oblivious computation
  • scalability
  • secret-sharing
  • verification

ASJC Scopus subject areas

  • Software
  • Computer Science Applications

Cite this

Gupta, P., Li, Y., Mehrotra, S., Panwar, N., & Sharma, S. (2020). Obscure: Information-Theoretically Secure, Oblivious, and Verifiable Aggregation Queries. In CODASPY 2020 - Proceedings of the 10th ACM Conference on Data and Application Security and Privacy (pp. 165-167). (CODASPY 2020 - Proceedings of the 10th ACM Conference on Data and Application Security and Privacy). Association for Computing Machinery, Inc. https://doi.org/10.1145/3374664.3379533