Gaussian Data Privacy Under Linear Function Recoverability
Ajaykrishnan Nageswaran · 2022 IEEE International Symposium on Information Theory (ISIT) · 2022
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 ℓ2-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.