Standard

Security versus privacy. / Farokhi, Farhad; Esfahani, Peyman Mohajerin.

Proceedings of the 57th IEEE Conference on Decision and Control (CDC 2018). ed. / Andrew R. Teel; Magnus Egerstedt. Piscataway, NJ, USA : IEEE, 2018. p. 7101-7106.

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

Harvard

Farokhi, F & Esfahani, PM 2018, Security versus privacy. in AR Teel & M Egerstedt (eds), Proceedings of the 57th IEEE Conference on Decision and Control (CDC 2018). IEEE, Piscataway, NJ, USA, pp. 7101-7106, CDC 2018: 57th IEEE Conference on Decision and Control, Miami, United States, 17/12/18. https://doi.org/10.1109/CDC.2018.8619460

APA

Farokhi, F., & Esfahani, P. M. (2018). Security versus privacy. In A. R. Teel, & M. Egerstedt (Eds.), Proceedings of the 57th IEEE Conference on Decision and Control (CDC 2018) (pp. 7101-7106). Piscataway, NJ, USA: IEEE. https://doi.org/10.1109/CDC.2018.8619460

Vancouver

Farokhi F, Esfahani PM. Security versus privacy. In Teel AR, Egerstedt M, editors, Proceedings of the 57th IEEE Conference on Decision and Control (CDC 2018). Piscataway, NJ, USA: IEEE. 2018. p. 7101-7106 https://doi.org/10.1109/CDC.2018.8619460

Author

Farokhi, Farhad ; Esfahani, Peyman Mohajerin. / Security versus privacy. Proceedings of the 57th IEEE Conference on Decision and Control (CDC 2018). editor / Andrew R. Teel ; Magnus Egerstedt. Piscataway, NJ, USA : IEEE, 2018. pp. 7101-7106

BibTeX

@inproceedings{9fd900cf50844463aa6023227317f75a,
title = "Security versus privacy",
abstract = "Linear queries can be submitted to a server containing private data. The server provides a response to the queries systematically corrupted using an additive noise to preserve the privacy of those whose data is stored on the server. The measure of privacy is inversely proportional to the trace of the Fisher information matrix. It is assumed that an adversary can inject a false bias to the responses. The measure of the security, capturing the ease of detecting the presence of the false data injection, is the sensitivity of the Kullback-Leiber divergence to the additive bias. An optimization problem for balancing privacy and security is proposed and subsequently solved. It is shown that the level of guaranteed privacy times the level of security equals a constant. Therefore, by increasing the level of privacy, the security guarantees can only be weakened and vice versa. Similar results are developed under the differential privacy framework.",
author = "Farhad Farokhi and Esfahani, {Peyman Mohajerin}",
year = "2018",
doi = "10.1109/CDC.2018.8619460",
language = "English",
pages = "7101--7106",
editor = "Teel, {Andrew R. } and Egerstedt, {Magnus }",
booktitle = "Proceedings of the 57th IEEE Conference on Decision and Control (CDC 2018)",
publisher = "IEEE",
address = "United States",

}

RIS

TY - GEN

T1 - Security versus privacy

AU - Farokhi, Farhad

AU - Esfahani, Peyman Mohajerin

PY - 2018

Y1 - 2018

N2 - Linear queries can be submitted to a server containing private data. The server provides a response to the queries systematically corrupted using an additive noise to preserve the privacy of those whose data is stored on the server. The measure of privacy is inversely proportional to the trace of the Fisher information matrix. It is assumed that an adversary can inject a false bias to the responses. The measure of the security, capturing the ease of detecting the presence of the false data injection, is the sensitivity of the Kullback-Leiber divergence to the additive bias. An optimization problem for balancing privacy and security is proposed and subsequently solved. It is shown that the level of guaranteed privacy times the level of security equals a constant. Therefore, by increasing the level of privacy, the security guarantees can only be weakened and vice versa. Similar results are developed under the differential privacy framework.

AB - Linear queries can be submitted to a server containing private data. The server provides a response to the queries systematically corrupted using an additive noise to preserve the privacy of those whose data is stored on the server. The measure of privacy is inversely proportional to the trace of the Fisher information matrix. It is assumed that an adversary can inject a false bias to the responses. The measure of the security, capturing the ease of detecting the presence of the false data injection, is the sensitivity of the Kullback-Leiber divergence to the additive bias. An optimization problem for balancing privacy and security is proposed and subsequently solved. It is shown that the level of guaranteed privacy times the level of security equals a constant. Therefore, by increasing the level of privacy, the security guarantees can only be weakened and vice versa. Similar results are developed under the differential privacy framework.

UR - http://www.scopus.com/inward/record.url?scp=85062166965&partnerID=8YFLogxK

U2 - 10.1109/CDC.2018.8619460

DO - 10.1109/CDC.2018.8619460

M3 - Conference contribution

SP - 7101

EP - 7106

BT - Proceedings of the 57th IEEE Conference on Decision and Control (CDC 2018)

A2 - Teel, Andrew R.

A2 - Egerstedt, Magnus

PB - IEEE

CY - Piscataway, NJ, USA

ER -

ID: 51915181