Gaussian Data Privacy Under Linear Function Recoverability
A user's data is represented by a Gaussian random variable. Given a linear function of the data, a querier is required to recover, with at least a prescribed accuracy level, the function value based on a query response provided by the user. The user devises the query response, subject to the re...
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| Vydáno v: | Proceedings / IEEE International Symposium on Information Theory s. 632 - 636 |
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| Hlavní autor: | |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
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IEEE
26.06.2022
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| Témata: | |
| ISSN: | 2157-8117 |
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| Abstract | A user's data is represented by a Gaussian random variable. Given a linear function of the data, a querier is required to recover, with at least a prescribed accuracy level, the function value based on a query response provided by the user. The user devises the query response, subject to the recoverability requirement, so as to maximize privacy of the data from the querier. Recoverability and privacy are both measured by ℓ 2 -distance criteria. An exact characterization is provided of maximum user data privacy under the recoverability condition. An explicit achievability scheme for the user is given and its privacy compared with a converse upper bound. |
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| AbstractList | A user's data is represented by a Gaussian random variable. Given a linear function of the data, a querier is required to recover, with at least a prescribed accuracy level, the function value based on a query response provided by the user. The user devises the query response, subject to the recoverability requirement, so as to maximize privacy of the data from the querier. Recoverability and privacy are both measured by ℓ 2 -distance criteria. An exact characterization is provided of maximum user data privacy under the recoverability condition. An explicit achievability scheme for the user is given and its privacy compared with a converse upper bound. |
| Author | Nageswaran, Ajaykrishnan |
| Author_xml | – sequence: 1 givenname: Ajaykrishnan surname: Nageswaran fullname: Nageswaran, Ajaykrishnan email: ajayk@umd.edu organization: University of Maryland,Department of Electrical and Computer Engineering and the Institute for Systems Research,College Park,MD,USA,20742 |
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| Snippet | A user's data is represented by a Gaussian random variable. Given a linear function of the data, a querier is required to recover, with at least a prescribed... |
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| StartPage | 632 |
| SubjectTerms | Data privacy Gaussian data privacy Information theory linear function computation Privacy query response Random variables recoverability Upper bound |
| Title | Gaussian Data Privacy Under Linear Function Recoverability |
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