TY - GEN
T1 - Partitioned data security on outsourced sensitive and non-sensitive data
AU - Mehrotra, Sharad
AU - Sharma, Shantanu
AU - Ullman, Jeffrey
AU - Mishra, Anurag
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/4
Y1 - 2019/4
N2 - 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.
AB - 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.
KW - Cryptographic techniques
KW - Data partitioning
KW - Inference attacks
KW - Partitioned data security
KW - Scalability
UR - http://www.scopus.com/inward/record.url?scp=85063875227&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063875227&partnerID=8YFLogxK
U2 - 10.1109/ICDE.2019.00064
DO - 10.1109/ICDE.2019.00064
M3 - Conference contribution
AN - SCOPUS:85063875227
T3 - Proceedings - International Conference on Data Engineering
SP - 650
EP - 661
BT - Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019
PB - IEEE Computer Society
T2 - 35th IEEE International Conference on Data Engineering, ICDE 2019
Y2 - 8 April 2019 through 11 April 2019
ER -