State-Based Differential Privacy Verification and Enforcement for Probabilistic Automata

Roughly speaking, differential privacy is a privacy-preserving strategy that guarantees attackers to be unlikely to infer, from the previous system output, the dataset from which an output is derived. This work introduces differential privacy to discrete event systems modeled by probabilistic automa...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Yuanxiu Teng [verfasserIn] Zhiwu Li [verfasserIn] Li Yin [verfasserIn] Naiqi Wu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	differential privacy discrete event system probabilistic automaton supervisory control privacy protection

Übergeordnetes Werk:	In: Mathematics - MDPI AG, 2013, 11(2023), 8, p 1853
Übergeordnetes Werk:	volume:11 ; year:2023 ; number:8, p 1853

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/math11081853

Katalog-ID:	DOAJ089817001

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allfieldsGer	10.3390/math11081853 doi (DE-627)DOAJ089817001 (DE-599)DOAJ7c7ee96835874033924e01c35bbefa92 DE-627 ger DE-627 rakwb eng QA1-939 Yuanxiu Teng verfasserin aut State-Based Differential Privacy Verification and Enforcement for Probabilistic Automata 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Roughly speaking, differential privacy is a privacy-preserving strategy that guarantees attackers to be unlikely to infer, from the previous system output, the dataset from which an output is derived. This work introduces differential privacy to discrete event systems modeled by probabilistic automata to protect the state information pertaining to system resource configurations. State differential privacy is defined to protect the initial state of a discrete event system, which represents its initial resource configuration. Step-based state differential privacy verification is proposed in the framework of probabilistic automata, such that an attacker is unlikely to determine the initial state from which a system evolves, within a finite step of observations, if two systems with two different initial states satisfy state differential privacy. Specifically, the probability distributions of generating observations within a finite step from the two different initial states are approximate. If the two systems do not satisfy state differential privacy, a control specification is proposed, such that state differential privacy is enforced via supervisory control that is maximally permissive. Experimental studies are given to illustrate that the proposed method can effectively verify state differential privacy and enforce privacy protection in the probabilistic automata framework. differential privacy discrete event system probabilistic automaton supervisory control privacy protection Mathematics Zhiwu Li verfasserin aut Li Yin verfasserin aut Naiqi Wu verfasserin aut In Mathematics MDPI AG, 2013 11(2023), 8, p 1853 (DE-627)737287764 (DE-600)2704244-3 22277390 nnns volume:11 year:2023 number:8, p 1853 https://doi.org/10.3390/math11081853 kostenfrei https://doaj.org/article/7c7ee96835874033924e01c35bbefa92 kostenfrei https://www.mdpi.com/2227-7390/11/8/1853 kostenfrei https://doaj.org/toc/2227-7390 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 8, p 1853
allfieldsSound	10.3390/math11081853 doi (DE-627)DOAJ089817001 (DE-599)DOAJ7c7ee96835874033924e01c35bbefa92 DE-627 ger DE-627 rakwb eng QA1-939 Yuanxiu Teng verfasserin aut State-Based Differential Privacy Verification and Enforcement for Probabilistic Automata 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Roughly speaking, differential privacy is a privacy-preserving strategy that guarantees attackers to be unlikely to infer, from the previous system output, the dataset from which an output is derived. This work introduces differential privacy to discrete event systems modeled by probabilistic automata to protect the state information pertaining to system resource configurations. State differential privacy is defined to protect the initial state of a discrete event system, which represents its initial resource configuration. Step-based state differential privacy verification is proposed in the framework of probabilistic automata, such that an attacker is unlikely to determine the initial state from which a system evolves, within a finite step of observations, if two systems with two different initial states satisfy state differential privacy. Specifically, the probability distributions of generating observations within a finite step from the two different initial states are approximate. If the two systems do not satisfy state differential privacy, a control specification is proposed, such that state differential privacy is enforced via supervisory control that is maximally permissive. Experimental studies are given to illustrate that the proposed method can effectively verify state differential privacy and enforce privacy protection in the probabilistic automata framework. differential privacy discrete event system probabilistic automaton supervisory control privacy protection Mathematics Zhiwu Li verfasserin aut Li Yin verfasserin aut Naiqi Wu verfasserin aut In Mathematics MDPI AG, 2013 11(2023), 8, p 1853 (DE-627)737287764 (DE-600)2704244-3 22277390 nnns volume:11 year:2023 number:8, p 1853 https://doi.org/10.3390/math11081853 kostenfrei https://doaj.org/article/7c7ee96835874033924e01c35bbefa92 kostenfrei https://www.mdpi.com/2227-7390/11/8/1853 kostenfrei https://doaj.org/toc/2227-7390 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 8, p 1853
language	English
source	In Mathematics 11(2023), 8, p 1853 volume:11 year:2023 number:8, p 1853
sourceStr	In Mathematics 11(2023), 8, p 1853 volume:11 year:2023 number:8, p 1853
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	differential privacy discrete event system probabilistic automaton supervisory control privacy protection Mathematics
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container_title	Mathematics
authorswithroles_txt_mv	Yuanxiu Teng @@aut@@ Zhiwu Li @@aut@@ Li Yin @@aut@@ Naiqi Wu @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
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callnumber-first	Q - Science
author	Yuanxiu Teng
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topic_title	QA1-939 State-Based Differential Privacy Verification and Enforcement for Probabilistic Automata differential privacy discrete event system probabilistic automaton supervisory control privacy protection
topic	misc QA1-939 misc differential privacy misc discrete event system misc probabilistic automaton misc supervisory control misc privacy protection misc Mathematics
topic_unstemmed	misc QA1-939 misc differential privacy misc discrete event system misc probabilistic automaton misc supervisory control misc privacy protection misc Mathematics
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title	State-Based Differential Privacy Verification and Enforcement for Probabilistic Automata
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abstract	Roughly speaking, differential privacy is a privacy-preserving strategy that guarantees attackers to be unlikely to infer, from the previous system output, the dataset from which an output is derived. This work introduces differential privacy to discrete event systems modeled by probabilistic automata to protect the state information pertaining to system resource configurations. State differential privacy is defined to protect the initial state of a discrete event system, which represents its initial resource configuration. Step-based state differential privacy verification is proposed in the framework of probabilistic automata, such that an attacker is unlikely to determine the initial state from which a system evolves, within a finite step of observations, if two systems with two different initial states satisfy state differential privacy. Specifically, the probability distributions of generating observations within a finite step from the two different initial states are approximate. If the two systems do not satisfy state differential privacy, a control specification is proposed, such that state differential privacy is enforced via supervisory control that is maximally permissive. Experimental studies are given to illustrate that the proposed method can effectively verify state differential privacy and enforce privacy protection in the probabilistic automata framework.
abstractGer	Roughly speaking, differential privacy is a privacy-preserving strategy that guarantees attackers to be unlikely to infer, from the previous system output, the dataset from which an output is derived. This work introduces differential privacy to discrete event systems modeled by probabilistic automata to protect the state information pertaining to system resource configurations. State differential privacy is defined to protect the initial state of a discrete event system, which represents its initial resource configuration. Step-based state differential privacy verification is proposed in the framework of probabilistic automata, such that an attacker is unlikely to determine the initial state from which a system evolves, within a finite step of observations, if two systems with two different initial states satisfy state differential privacy. Specifically, the probability distributions of generating observations within a finite step from the two different initial states are approximate. If the two systems do not satisfy state differential privacy, a control specification is proposed, such that state differential privacy is enforced via supervisory control that is maximally permissive. Experimental studies are given to illustrate that the proposed method can effectively verify state differential privacy and enforce privacy protection in the probabilistic automata framework.
abstract_unstemmed	Roughly speaking, differential privacy is a privacy-preserving strategy that guarantees attackers to be unlikely to infer, from the previous system output, the dataset from which an output is derived. This work introduces differential privacy to discrete event systems modeled by probabilistic automata to protect the state information pertaining to system resource configurations. State differential privacy is defined to protect the initial state of a discrete event system, which represents its initial resource configuration. Step-based state differential privacy verification is proposed in the framework of probabilistic automata, such that an attacker is unlikely to determine the initial state from which a system evolves, within a finite step of observations, if two systems with two different initial states satisfy state differential privacy. Specifically, the probability distributions of generating observations within a finite step from the two different initial states are approximate. If the two systems do not satisfy state differential privacy, a control specification is proposed, such that state differential privacy is enforced via supervisory control that is maximally permissive. Experimental studies are given to illustrate that the proposed method can effectively verify state differential privacy and enforce privacy protection in the probabilistic automata framework.
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State-Based Differential Privacy Verification and Enforcement for Probabilistic Automata

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