TY - JOUR
T1 - Towards Privacy Preserving Publishing of Set-Valued Data on Hybrid Cloud
AU - Zhang, Hongli
AU - Zhou, Zhigang
AU - Ye, Lin
AU - Du, Xiaojiang
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2018/4/1
Y1 - 2018/4/1
N2 - Storage as a service has become an important paradigm in cloud computing for its great flexibility and economic savings. However, the development is hampered by data privacy concerns: data owners no longer physically possess the storage of their data. In this work, we study the issue of privacy-preserving set-valued data publishing. Existing data privacy-preserving techniques (such as encryption, suppression, generalization) are not applicable in many real scenes, since they would incur large overhead for data query or high information loss. Motivated by this observation, we present a suite of new techniques that make privacy-aware set-valued data publishing feasible on hybrid cloud. On data publishing phase, we propose a data partition technique, named extended quasi-identifier-partitioning (EQI-partitioning), which disassociates record terms that participate in identifying combinations. This way the cloud server cannot associate with high probability a record with rare term combinations. We prove the privacy guarantee of our mechanism. On data querying phase, we adopt interactive differential privacy strategy to resist privacy breaches from statistical queries. We finally evaluate its performance using real-life data sets on our cloud test-bed. Our extensive experiments demonstrate the validity and practicality of the proposed scheme.
AB - Storage as a service has become an important paradigm in cloud computing for its great flexibility and economic savings. However, the development is hampered by data privacy concerns: data owners no longer physically possess the storage of their data. In this work, we study the issue of privacy-preserving set-valued data publishing. Existing data privacy-preserving techniques (such as encryption, suppression, generalization) are not applicable in many real scenes, since they would incur large overhead for data query or high information loss. Motivated by this observation, we present a suite of new techniques that make privacy-aware set-valued data publishing feasible on hybrid cloud. On data publishing phase, we propose a data partition technique, named extended quasi-identifier-partitioning (EQI-partitioning), which disassociates record terms that participate in identifying combinations. This way the cloud server cannot associate with high probability a record with rare term combinations. We prove the privacy guarantee of our mechanism. On data querying phase, we adopt interactive differential privacy strategy to resist privacy breaches from statistical queries. We finally evaluate its performance using real-life data sets on our cloud test-bed. Our extensive experiments demonstrate the validity and practicality of the proposed scheme.
KW - Cloud computing
KW - data partition
KW - data privacy
KW - differential privacy
KW - hybrid cloud
KW - set-valued data
UR - http://www.scopus.com/inward/record.url?scp=85048236868&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048236868&partnerID=8YFLogxK
U2 - 10.1109/TCC.2015.2430316
DO - 10.1109/TCC.2015.2430316
M3 - Article
AN - SCOPUS:85048236868
VL - 6
SP - 316
EP - 329
JO - IEEE Transactions on Cloud Computing
JF - IEEE Transactions on Cloud Computing
IS - 2
ER -