TY - GEN
T1 - Secure Data Outsourcing with Adversarial Data Dependency Constraints
AU - Dong, Boxiang
AU - Wang, Wendy
AU - Yang, Jie
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/6/30
Y1 - 2016/6/30
N2 - Cloud computing enables end-users to outsource their dataset and data management needs to a third-party service provider. One of the major security concerns of the outsourcing paradigm is how to protect sensitive information in the outsourced dataset. In general, the sensitive information can be protected by encryption. However, data dependency constraints in the outsourced data may serve as adversary knowledge and bring security vulnerabilities. In this paper, we focus on functional dependency (FD), an important type of data dependency constraints, and study the security threats by the adversarial FDs. We design the practical scheme that can defend against the FD attack by encrypting a small amount of non-sensitive data (encryption overhead). We prove that searching for the scheme that leads to the optimal encryption overhead is NP-complete, and design efficient heuristic algorithms. We conduct an extensive set of experiments on two real-world datasets. The experiment results show that our heuristic approach brings small amounts of encryption overhead (at most 1% more than the optimal overhead), and enjoys a ten times speedup compared with the optimal solution. Besides, our approach can reduce up to 90% of the encryption overhead of the-state-of-art solution.
AB - Cloud computing enables end-users to outsource their dataset and data management needs to a third-party service provider. One of the major security concerns of the outsourcing paradigm is how to protect sensitive information in the outsourced dataset. In general, the sensitive information can be protected by encryption. However, data dependency constraints in the outsourced data may serve as adversary knowledge and bring security vulnerabilities. In this paper, we focus on functional dependency (FD), an important type of data dependency constraints, and study the security threats by the adversarial FDs. We design the practical scheme that can defend against the FD attack by encrypting a small amount of non-sensitive data (encryption overhead). We prove that searching for the scheme that leads to the optimal encryption overhead is NP-complete, and design efficient heuristic algorithms. We conduct an extensive set of experiments on two real-world datasets. The experiment results show that our heuristic approach brings small amounts of encryption overhead (at most 1% more than the optimal overhead), and enjoys a ten times speedup compared with the optimal solution. Besides, our approach can reduce up to 90% of the encryption overhead of the-state-of-art solution.
KW - cloud computing
KW - data security
KW - functional dependency
UR - http://www.scopus.com/inward/record.url?scp=84979763127&partnerID=8YFLogxK
U2 - 10.1109/BigDataSecurity-HPSC-IDS.2016.17
DO - 10.1109/BigDataSecurity-HPSC-IDS.2016.17
M3 - Conference contribution
AN - SCOPUS:84979763127
T3 - Proceedings - 2nd IEEE International Conference on Big Data Security on Cloud, IEEE BigDataSecurity 2016, 2nd IEEE International Conference on High Performance and Smart Computing, IEEE HPSC 2016 and IEEE International Conference on Intelligent Data and Security, IEEE IDS 2016
SP - 73
EP - 78
BT - Proceedings - 2nd IEEE International Conference on Big Data Security on Cloud, IEEE BigDataSecurity 2016, 2nd IEEE International Conference on High Performance and Smart Computing, IEEE HPSC 2016 and IEEE International Conference on Intelligent Data and Security, IEEE IDS 2016
A2 - Qiu, Meikang
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE International Conference on Big Data Security on Cloud, IEEE BigDataSecurity 2016, 2nd IEEE International Conference on High Performance and Smart Computing, IEEE HPSC 2016 and IEEE International Conference on Intelligent Data and Security, IEEE IDS 2016
Y2 - 9 April 2016 through 10 April 2016
ER -