Partitioned Data Security on Outsourced Sensitive and Non-sensitive Data
Abstract
Despite extensive research on cryptography, secure and efficient query processing over outsourced data remains an open challenge. This paper continues along 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 propose a new secure approach, entitled query binning (QB) that allows non-sensitive parts of the data to be outsourced in clear-text while guaranteeing that no information is leaked by the joint processing of non-sensitive data (in clear-text) and sensitive data (in encrypted form). 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. Interestingly, in addition to improve performance, we show that QB actually strengthens the security of the underlying cryptographic technique by preventing size, frequency-count, and workload-skew attacks.
Cite
@article{arxiv.1812.09233,
title = {Partitioned Data Security on Outsourced Sensitive and Non-sensitive Data},
author = {Sharad Mehrotra and Shantanu Sharma and Jeffrey D. Ullman and Anurag Mishra},
journal= {arXiv preprint arXiv:1812.09233},
year = {2018}
}
Comments
Accepted in IEEE International Conference on Data Engineering (ICDE), 2019. arXiv admin note: text overlap with arXiv:1812.01741