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Inferential Privacy: From Impossibility to Database Privacy. (arXiv:2303.07782v1 [cs.CR])
Web: http://arxiv.org/abs/2303.07782
March 15, 2023, 1:10 a.m. | Sara Saeidian (1), Giulia Cervia (2), Tobias J. Oechtering (1), Mikael Skoglund (1) ((1) KTH Royal Institute of Technology, (2) IMT Nord Europe)
cs.CR updates on arXiv.org arxiv.org
We investigate the possibility of guaranteeing inferential privacy for
mechanisms that release useful information about some data containing sensitive
information, denoted by $X$. We describe a general model of utility and privacy
in which utility is achieved by disclosing the value of low-entropy features of
$X$, while privacy is maintained by keeping high-entropy features of $X$
secret. Adopting this model, we prove that meaningful inferential privacy
guarantees can be obtained, even though this is commonly considered to be
impossible by …
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