Secure Data Outsourcing with Adversarial Data Dependency Constraints

Boxiang Dong, Wendy Wang, Jie Yang

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 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
EditorsMeikang Qiu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages73-78
Number of pages6
ISBN (Electronic)9781509024025
DOIs
StatePublished - 30 Jun 2016
Event2nd 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 - New York, United States
Duration: 9 Apr 201610 Apr 2016

Other

Other2nd 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
CountryUnited States
CityNew York
Period9/04/1610/04/16

Fingerprint

Outsourcing
Cryptography
Heuristic algorithms
Cloud computing
Information management
Experiments
Encryption
Experiment

Keywords

  • cloud computing
  • data security
  • functional dependency

Cite this

Dong, B., Wang, W., & Yang, J. (2016). Secure Data Outsourcing with Adversarial Data Dependency Constraints. In M. Qiu (Ed.), 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 (pp. 73-78). [7502267] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigDataSecurity-HPSC-IDS.2016.17
Dong, Boxiang ; Wang, Wendy ; Yang, Jie. / Secure Data Outsourcing with Adversarial Data Dependency Constraints. 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. editor / Meikang Qiu. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 73-78
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abstract = "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.",
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Dong, B, Wang, W & Yang, J 2016, Secure Data Outsourcing with Adversarial Data Dependency Constraints. in M Qiu (ed.), 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., 7502267, Institute of Electrical and Electronics Engineers Inc., pp. 73-78, 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, New York, United States, 9/04/16. https://doi.org/10.1109/BigDataSecurity-HPSC-IDS.2016.17

Secure Data Outsourcing with Adversarial Data Dependency Constraints. / Dong, Boxiang; Wang, Wendy; Yang, Jie.

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. ed. / Meikang Qiu. Institute of Electrical and Electronics Engineers Inc., 2016. p. 73-78 7502267.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

TY - GEN

T1 - Secure Data Outsourcing with Adversarial Data Dependency Constraints

AU - Dong, Boxiang

AU - Wang, Wendy

AU - Yang, Jie

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.

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M3 - Conference contribution

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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.

ER -

Dong B, Wang W, Yang J. Secure Data Outsourcing with Adversarial Data Dependency Constraints. In Qiu M, editor, 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. Institute of Electrical and Electronics Engineers Inc. 2016. p. 73-78. 7502267 https://doi.org/10.1109/BigDataSecurity-HPSC-IDS.2016.17