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On Confidentiality Preserving Monitoring of Linear Dynamic Networks Against Inference Attacks

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On Confidentiality Preserving Monitoring of Linear Dynamic Networks Against Inference Attacks. / Lu, Yang.
American Control Conference. IEEE, 2015. p. 359-364.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Lu, Y 2015, On Confidentiality Preserving Monitoring of Linear Dynamic Networks Against Inference Attacks. in American Control Conference. IEEE, pp. 359-364, 2015 American Control Conference (ACC), Chicago, Illinois, United States, 1/07/15. https://doi.org/10.1109/ACC.2015.7170762

APA

Lu, Y. (2015). On Confidentiality Preserving Monitoring of Linear Dynamic Networks Against Inference Attacks. In American Control Conference (pp. 359-364). IEEE. https://doi.org/10.1109/ACC.2015.7170762

Vancouver

Lu Y. On Confidentiality Preserving Monitoring of Linear Dynamic Networks Against Inference Attacks. In American Control Conference. IEEE. 2015. p. 359-364 doi: 10.1109/ACC.2015.7170762

Author

Lu, Yang. / On Confidentiality Preserving Monitoring of Linear Dynamic Networks Against Inference Attacks. American Control Conference. IEEE, 2015. pp. 359-364

Bibtex

@inproceedings{5c193596dfaf43cca14835047316f62d,
title = "On Confidentiality Preserving Monitoring of Linear Dynamic Networks Against Inference Attacks",
abstract = "Distributed information sharing in dynamic networks is ubiquitous. It raises the concern that confidential information of dynamic networks could be leaked to malicious entities and further exploited in direct attacks. In this paper, we formulate the problem of competitive confidentiality preserving monitoring of linear dynamic networks against inference attacks. We show that the unstructured ℓ 0 minimization is NP-hard. We then provide a SDP equivalence for the structured ℓ 2 minimization.",
author = "Yang Lu",
year = "2015",
month = jul,
day = "3",
doi = "10.1109/ACC.2015.7170762",
language = "English",
pages = "359--364",
booktitle = "American Control Conference",
publisher = "IEEE",
note = "2015 American Control Conference (ACC) ; Conference date: 01-07-2015 Through 03-07-2015",
url = "https://ieeexplore.ieee.org/xpl/conhome/7160954/proceeding",

}

RIS

TY - GEN

T1 - On Confidentiality Preserving Monitoring of Linear Dynamic Networks Against Inference Attacks

AU - Lu, Yang

PY - 2015/7/3

Y1 - 2015/7/3

N2 - Distributed information sharing in dynamic networks is ubiquitous. It raises the concern that confidential information of dynamic networks could be leaked to malicious entities and further exploited in direct attacks. In this paper, we formulate the problem of competitive confidentiality preserving monitoring of linear dynamic networks against inference attacks. We show that the unstructured ℓ 0 minimization is NP-hard. We then provide a SDP equivalence for the structured ℓ 2 minimization.

AB - Distributed information sharing in dynamic networks is ubiquitous. It raises the concern that confidential information of dynamic networks could be leaked to malicious entities and further exploited in direct attacks. In this paper, we formulate the problem of competitive confidentiality preserving monitoring of linear dynamic networks against inference attacks. We show that the unstructured ℓ 0 minimization is NP-hard. We then provide a SDP equivalence for the structured ℓ 2 minimization.

U2 - 10.1109/ACC.2015.7170762

DO - 10.1109/ACC.2015.7170762

M3 - Conference contribution/Paper

SP - 359

EP - 364

BT - American Control Conference

PB - IEEE

T2 - 2015 American Control Conference (ACC)

Y2 - 1 July 2015 through 3 July 2015

ER -