OBSCURE: Information-theoretic oblivious and verifiable aggregation queries

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

Research output: Contribution to journalConference articlepeer-review

10 Scopus citations


Despite extensive research on cryptography, secure and efficient query processing over outsourced data remains an open challenge. We develop communication-efficient and information-theoretically secure algorithms for privacy-preserving aggregation queries using multi-party computation (MPC). Specifically, query processing techniques over secret-shared data outsourced by single or multiple database owners are developed. These algorithms allow a user to execute queries on the secret-shared database and also prevent the network and the (adversarial) clouds to learn the user's queries, results, or the database. We further develop (non-mandatory) privacypreserving result verification algorithms that detect malicious behaviors, and experimentally validate the efficiency of our approach over large datasets, the size of which prior approaches to secretsharing or MPC systems have not scaled to.

Original languageEnglish (US)
Pages (from-to)1030-1043
Number of pages14
JournalProceedings of the VLDB Endowment
Issue number9
StatePublished - 2018
Externally publishedYes
Event45th International Conference on Very Large Data Bases, VLDB 2019 - Los Angeles, United States
Duration: Aug 26 2017Aug 30 2017

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • General Computer Science


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