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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Published in:Proceedings / IEEE International Symposium on Information Theory pp. 632 - 636
Main Author: Nageswaran, Ajaykrishnan
Format: Conference Proceeding
Language:English
Published: IEEE 26.06.2022
Subjects:
Data privacy
Gaussian data privacy
Information theory
linear function computation
Privacy
query response
Random variables
recoverability
Upper bound
ISSN:2157-8117
Online Access:Get full text
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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.
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
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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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