PANDA: Partitioned Data Security on Outsourced Sensitive and Non-sensitive Data

Sharad Mehrotra, Shantanu Sharma, Jeffrey D. Ullman, Dhrubajyoti Ghosh, Peeyush Gupta, Anurag Mishra

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Despite extensive research on cryptography, secure and efficient query processing over outsourced data remains an open challenge. This article continues along with the emerging trend in secure data processing that recognizes that the entire dataset may not be sensitive and, hence, non-sensitivity of data can be exploited to overcome limitations of existing encryption-based approaches. We first provide a new security definition, entitled partitioned data security, for guaranteeing that the joint processing of non-sensitive data (in cleartext) and sensitive data (in encrypted form) does not lead to any leakage. Then, this article proposes a new secure approach, entitled query binning (QB), that allows secure execution of queries over non-sensitive and sensitive parts of the data. QB maps a query to a set of queries over the sensitive and non-sensitive data in a way that no leakage will occur due to the joint processing over sensitive and non-sensitive data. In particular, we propose secure algorithms for selection, range, and join queries to be executed over encrypted sensitive and cleartext non-sensitive datasets. Interestingly, in addition to improving performance, we show that QB actually strengthens the security of the underlying cryptographic technique by preventing size, frequency-count, and workload-skew attacks.

Original languageEnglish (US)
Article number23
JournalACM Transactions on Management Information Systems
Issue number4
StatePublished - Dec 2020
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Management Information Systems
  • General Computer Science


  • Data outsourcing
  • Intel SGX
  • data encryption
  • data partitioning
  • multi-party computation
  • non-sensitive data
  • output-size attack
  • scalable cryptography
  • secret-sharing
  • sensitive data
  • workload skew attack


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