The Value of Privacy
We study the value of data privacy in a game-theoretic model of trading private data, where a data collector purchases private data from strategic data subjects (individuals) through an incentive mechanism. The private data of each individual represents her knowledge about an underlying state, which...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wang, Weina [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Performance evaluation review - New York, NY : ACM, 1972, 44(2016), 1, Seite 249-260 |
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| Übergeordnetes Werk: |
volume:44 ; year:2016 ; number:1 ; pages:249-260 |
| Links: |
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| DOI / URN: |
10.1145/2964791.2901461 |
|---|
| Katalog-ID: |
OLC1976722322 |
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| 520 | |a We study the value of data privacy in a game-theoretic model of trading private data, where a data collector purchases private data from strategic data subjects (individuals) through an incentive mechanism. The private data of each individual represents her knowledge about an underlying state, which is the information that the data collector desires to learn. Different from most of the existing work on privacy-aware surveys, our model does not assume the data collector to be trustworthy. Then, an individual takes full control of its own data privacy and reports only a privacy-preserving version of her data. In this paper, the value of ε units of privacy is measured by the minimum payment of all nonnegative payment mechanisms, under which an individual's best response at a Nash equilibrium is to report the data with a privacy level of ε. The higher ε is, the less private the reported data is. We derive lower and upper bounds on the value of privacy which are asymptotically tight as the number of data subjects becomes large. Specifically, the lower bound assures that it is impossible to use less amount of payment to buy ε units of privacy, and the upper bound is given by an achievable payment mechanism that we designed. Based on these fundamental limits, we further derive lower and upper bounds on the minimum total payment for the data collector to achieve a given learning accuracy target, and show that the total payment of the designed mechanism is at most one individual's payment away from the minimum. | ||
| 650 | 4 | |a differential privacy | |
| 650 | 4 | |a incentive mechanism | |
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| 700 | 1 | |a Ying, Lei |4 oth | |
| 700 | 1 | |a Zhang, Junshan |4 oth | |
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10.1145/2964791.2901461 doi PQ20160719 (DE-627)OLC1976722322 (DE-599)GBVOLC1976722322 (PRQ)a591-f4dec6ab5a69d8c54c81c84b630c220adf98d23357ae13bb67e60e60a40122660 (KEY)0012195920160000044000100249valueofprivacy DE-627 ger DE-627 rakwb eng 620 004 DNB 54.33 bkl 54.00 bkl Wang, Weina verfasserin aut The Value of Privacy 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier We study the value of data privacy in a game-theoretic model of trading private data, where a data collector purchases private data from strategic data subjects (individuals) through an incentive mechanism. The private data of each individual represents her knowledge about an underlying state, which is the information that the data collector desires to learn. Different from most of the existing work on privacy-aware surveys, our model does not assume the data collector to be trustworthy. Then, an individual takes full control of its own data privacy and reports only a privacy-preserving version of her data. In this paper, the value of ε units of privacy is measured by the minimum payment of all nonnegative payment mechanisms, under which an individual's best response at a Nash equilibrium is to report the data with a privacy level of ε. The higher ε is, the less private the reported data is. We derive lower and upper bounds on the value of privacy which are asymptotically tight as the number of data subjects becomes large. Specifically, the lower bound assures that it is impossible to use less amount of payment to buy ε units of privacy, and the upper bound is given by an achievable payment mechanism that we designed. Based on these fundamental limits, we further derive lower and upper bounds on the minimum total payment for the data collector to achieve a given learning accuracy target, and show that the total payment of the designed mechanism is at most one individual's payment away from the minimum. differential privacy incentive mechanism mechanism design strategic data subjects game theory Ying, Lei oth Zhang, Junshan oth Enthalten in Performance evaluation review New York, NY : ACM, 1972 44(2016), 1, Seite 249-260 (DE-627)12945575X (DE-600)199353-7 (DE-576)014819090 0163-5999 nnns volume:44 year:2016 number:1 pages:249-260 http://dx.doi.org/10.1145/2964791.2901461 Volltext http://dl.acm.org/citation.cfm?id=2901461 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2021 GBV_ILN_2190 54.33 AVZ 54.00 AVZ AR 44 2016 1 249-260 |
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10.1145/2964791.2901461 doi PQ20160719 (DE-627)OLC1976722322 (DE-599)GBVOLC1976722322 (PRQ)a591-f4dec6ab5a69d8c54c81c84b630c220adf98d23357ae13bb67e60e60a40122660 (KEY)0012195920160000044000100249valueofprivacy DE-627 ger DE-627 rakwb eng 620 004 DNB 54.33 bkl 54.00 bkl Wang, Weina verfasserin aut The Value of Privacy 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier We study the value of data privacy in a game-theoretic model of trading private data, where a data collector purchases private data from strategic data subjects (individuals) through an incentive mechanism. The private data of each individual represents her knowledge about an underlying state, which is the information that the data collector desires to learn. Different from most of the existing work on privacy-aware surveys, our model does not assume the data collector to be trustworthy. Then, an individual takes full control of its own data privacy and reports only a privacy-preserving version of her data. In this paper, the value of ε units of privacy is measured by the minimum payment of all nonnegative payment mechanisms, under which an individual's best response at a Nash equilibrium is to report the data with a privacy level of ε. The higher ε is, the less private the reported data is. We derive lower and upper bounds on the value of privacy which are asymptotically tight as the number of data subjects becomes large. Specifically, the lower bound assures that it is impossible to use less amount of payment to buy ε units of privacy, and the upper bound is given by an achievable payment mechanism that we designed. Based on these fundamental limits, we further derive lower and upper bounds on the minimum total payment for the data collector to achieve a given learning accuracy target, and show that the total payment of the designed mechanism is at most one individual's payment away from the minimum. differential privacy incentive mechanism mechanism design strategic data subjects game theory Ying, Lei oth Zhang, Junshan oth Enthalten in Performance evaluation review New York, NY : ACM, 1972 44(2016), 1, Seite 249-260 (DE-627)12945575X (DE-600)199353-7 (DE-576)014819090 0163-5999 nnns volume:44 year:2016 number:1 pages:249-260 http://dx.doi.org/10.1145/2964791.2901461 Volltext http://dl.acm.org/citation.cfm?id=2901461 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2021 GBV_ILN_2190 54.33 AVZ 54.00 AVZ AR 44 2016 1 249-260 |
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10.1145/2964791.2901461 doi PQ20160719 (DE-627)OLC1976722322 (DE-599)GBVOLC1976722322 (PRQ)a591-f4dec6ab5a69d8c54c81c84b630c220adf98d23357ae13bb67e60e60a40122660 (KEY)0012195920160000044000100249valueofprivacy DE-627 ger DE-627 rakwb eng 620 004 DNB 54.33 bkl 54.00 bkl Wang, Weina verfasserin aut The Value of Privacy 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier We study the value of data privacy in a game-theoretic model of trading private data, where a data collector purchases private data from strategic data subjects (individuals) through an incentive mechanism. The private data of each individual represents her knowledge about an underlying state, which is the information that the data collector desires to learn. Different from most of the existing work on privacy-aware surveys, our model does not assume the data collector to be trustworthy. Then, an individual takes full control of its own data privacy and reports only a privacy-preserving version of her data. In this paper, the value of ε units of privacy is measured by the minimum payment of all nonnegative payment mechanisms, under which an individual's best response at a Nash equilibrium is to report the data with a privacy level of ε. The higher ε is, the less private the reported data is. We derive lower and upper bounds on the value of privacy which are asymptotically tight as the number of data subjects becomes large. Specifically, the lower bound assures that it is impossible to use less amount of payment to buy ε units of privacy, and the upper bound is given by an achievable payment mechanism that we designed. Based on these fundamental limits, we further derive lower and upper bounds on the minimum total payment for the data collector to achieve a given learning accuracy target, and show that the total payment of the designed mechanism is at most one individual's payment away from the minimum. differential privacy incentive mechanism mechanism design strategic data subjects game theory Ying, Lei oth Zhang, Junshan oth Enthalten in Performance evaluation review New York, NY : ACM, 1972 44(2016), 1, Seite 249-260 (DE-627)12945575X (DE-600)199353-7 (DE-576)014819090 0163-5999 nnns volume:44 year:2016 number:1 pages:249-260 http://dx.doi.org/10.1145/2964791.2901461 Volltext http://dl.acm.org/citation.cfm?id=2901461 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2021 GBV_ILN_2190 54.33 AVZ 54.00 AVZ AR 44 2016 1 249-260 |
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10.1145/2964791.2901461 doi PQ20160719 (DE-627)OLC1976722322 (DE-599)GBVOLC1976722322 (PRQ)a591-f4dec6ab5a69d8c54c81c84b630c220adf98d23357ae13bb67e60e60a40122660 (KEY)0012195920160000044000100249valueofprivacy DE-627 ger DE-627 rakwb eng 620 004 DNB 54.33 bkl 54.00 bkl Wang, Weina verfasserin aut The Value of Privacy 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier We study the value of data privacy in a game-theoretic model of trading private data, where a data collector purchases private data from strategic data subjects (individuals) through an incentive mechanism. The private data of each individual represents her knowledge about an underlying state, which is the information that the data collector desires to learn. Different from most of the existing work on privacy-aware surveys, our model does not assume the data collector to be trustworthy. Then, an individual takes full control of its own data privacy and reports only a privacy-preserving version of her data. In this paper, the value of ε units of privacy is measured by the minimum payment of all nonnegative payment mechanisms, under which an individual's best response at a Nash equilibrium is to report the data with a privacy level of ε. The higher ε is, the less private the reported data is. We derive lower and upper bounds on the value of privacy which are asymptotically tight as the number of data subjects becomes large. Specifically, the lower bound assures that it is impossible to use less amount of payment to buy ε units of privacy, and the upper bound is given by an achievable payment mechanism that we designed. Based on these fundamental limits, we further derive lower and upper bounds on the minimum total payment for the data collector to achieve a given learning accuracy target, and show that the total payment of the designed mechanism is at most one individual's payment away from the minimum. differential privacy incentive mechanism mechanism design strategic data subjects game theory Ying, Lei oth Zhang, Junshan oth Enthalten in Performance evaluation review New York, NY : ACM, 1972 44(2016), 1, Seite 249-260 (DE-627)12945575X (DE-600)199353-7 (DE-576)014819090 0163-5999 nnns volume:44 year:2016 number:1 pages:249-260 http://dx.doi.org/10.1145/2964791.2901461 Volltext http://dl.acm.org/citation.cfm?id=2901461 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2021 GBV_ILN_2190 54.33 AVZ 54.00 AVZ AR 44 2016 1 249-260 |
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10.1145/2964791.2901461 doi PQ20160719 (DE-627)OLC1976722322 (DE-599)GBVOLC1976722322 (PRQ)a591-f4dec6ab5a69d8c54c81c84b630c220adf98d23357ae13bb67e60e60a40122660 (KEY)0012195920160000044000100249valueofprivacy DE-627 ger DE-627 rakwb eng 620 004 DNB 54.33 bkl 54.00 bkl Wang, Weina verfasserin aut The Value of Privacy 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier We study the value of data privacy in a game-theoretic model of trading private data, where a data collector purchases private data from strategic data subjects (individuals) through an incentive mechanism. The private data of each individual represents her knowledge about an underlying state, which is the information that the data collector desires to learn. Different from most of the existing work on privacy-aware surveys, our model does not assume the data collector to be trustworthy. Then, an individual takes full control of its own data privacy and reports only a privacy-preserving version of her data. In this paper, the value of ε units of privacy is measured by the minimum payment of all nonnegative payment mechanisms, under which an individual's best response at a Nash equilibrium is to report the data with a privacy level of ε. The higher ε is, the less private the reported data is. We derive lower and upper bounds on the value of privacy which are asymptotically tight as the number of data subjects becomes large. Specifically, the lower bound assures that it is impossible to use less amount of payment to buy ε units of privacy, and the upper bound is given by an achievable payment mechanism that we designed. Based on these fundamental limits, we further derive lower and upper bounds on the minimum total payment for the data collector to achieve a given learning accuracy target, and show that the total payment of the designed mechanism is at most one individual's payment away from the minimum. differential privacy incentive mechanism mechanism design strategic data subjects game theory Ying, Lei oth Zhang, Junshan oth Enthalten in Performance evaluation review New York, NY : ACM, 1972 44(2016), 1, Seite 249-260 (DE-627)12945575X (DE-600)199353-7 (DE-576)014819090 0163-5999 nnns volume:44 year:2016 number:1 pages:249-260 http://dx.doi.org/10.1145/2964791.2901461 Volltext http://dl.acm.org/citation.cfm?id=2901461 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2021 GBV_ILN_2190 54.33 AVZ 54.00 AVZ AR 44 2016 1 249-260 |
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The Value of Privacy |
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Wang, Weina |
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Performance evaluation review |
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Wang, Weina |
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10.1145/2964791.2901461 |
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620 004 |
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value of privacy |
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The Value of Privacy |
| abstract |
We study the value of data privacy in a game-theoretic model of trading private data, where a data collector purchases private data from strategic data subjects (individuals) through an incentive mechanism. The private data of each individual represents her knowledge about an underlying state, which is the information that the data collector desires to learn. Different from most of the existing work on privacy-aware surveys, our model does not assume the data collector to be trustworthy. Then, an individual takes full control of its own data privacy and reports only a privacy-preserving version of her data. In this paper, the value of ε units of privacy is measured by the minimum payment of all nonnegative payment mechanisms, under which an individual's best response at a Nash equilibrium is to report the data with a privacy level of ε. The higher ε is, the less private the reported data is. We derive lower and upper bounds on the value of privacy which are asymptotically tight as the number of data subjects becomes large. Specifically, the lower bound assures that it is impossible to use less amount of payment to buy ε units of privacy, and the upper bound is given by an achievable payment mechanism that we designed. Based on these fundamental limits, we further derive lower and upper bounds on the minimum total payment for the data collector to achieve a given learning accuracy target, and show that the total payment of the designed mechanism is at most one individual's payment away from the minimum. |
| abstractGer |
We study the value of data privacy in a game-theoretic model of trading private data, where a data collector purchases private data from strategic data subjects (individuals) through an incentive mechanism. The private data of each individual represents her knowledge about an underlying state, which is the information that the data collector desires to learn. Different from most of the existing work on privacy-aware surveys, our model does not assume the data collector to be trustworthy. Then, an individual takes full control of its own data privacy and reports only a privacy-preserving version of her data. In this paper, the value of ε units of privacy is measured by the minimum payment of all nonnegative payment mechanisms, under which an individual's best response at a Nash equilibrium is to report the data with a privacy level of ε. The higher ε is, the less private the reported data is. We derive lower and upper bounds on the value of privacy which are asymptotically tight as the number of data subjects becomes large. Specifically, the lower bound assures that it is impossible to use less amount of payment to buy ε units of privacy, and the upper bound is given by an achievable payment mechanism that we designed. Based on these fundamental limits, we further derive lower and upper bounds on the minimum total payment for the data collector to achieve a given learning accuracy target, and show that the total payment of the designed mechanism is at most one individual's payment away from the minimum. |
| abstract_unstemmed |
We study the value of data privacy in a game-theoretic model of trading private data, where a data collector purchases private data from strategic data subjects (individuals) through an incentive mechanism. The private data of each individual represents her knowledge about an underlying state, which is the information that the data collector desires to learn. Different from most of the existing work on privacy-aware surveys, our model does not assume the data collector to be trustworthy. Then, an individual takes full control of its own data privacy and reports only a privacy-preserving version of her data. In this paper, the value of ε units of privacy is measured by the minimum payment of all nonnegative payment mechanisms, under which an individual's best response at a Nash equilibrium is to report the data with a privacy level of ε. The higher ε is, the less private the reported data is. We derive lower and upper bounds on the value of privacy which are asymptotically tight as the number of data subjects becomes large. Specifically, the lower bound assures that it is impossible to use less amount of payment to buy ε units of privacy, and the upper bound is given by an achievable payment mechanism that we designed. Based on these fundamental limits, we further derive lower and upper bounds on the minimum total payment for the data collector to achieve a given learning accuracy target, and show that the total payment of the designed mechanism is at most one individual's payment away from the minimum. |
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The Value of Privacy |
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http://dx.doi.org/10.1145/2964791.2901461 http://dl.acm.org/citation.cfm?id=2901461 |
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Ying, Lei Zhang, Junshan |
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2024-07-03T16:28:17.889Z |
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