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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2307.05828 |
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| _version_ | 1866911943512555520 |
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| author | Nageswaran, Ajaykrishnan Narayan, Prakash |
| author_facet | Nageswaran, Ajaykrishnan Narayan, Prakash |
| contents | For a given function of user data, a querier must recover with at least a prescribed probability, the value of the function based on a user-provided query response. Subject to this requirement, the user forms the query response so as to minimize the likelihood of the querier guessing a list of prescribed size to which the data value belongs based on the query response. We obtain a general converse upper bound for maximum list privacy. This bound is shown to be tight for the case of a binary-valued function through an explicit achievability scheme that involves an add-noise query response. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_05828 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | List Privacy Under Function Recoverability Nageswaran, Ajaykrishnan Narayan, Prakash Information Theory For a given function of user data, a querier must recover with at least a prescribed probability, the value of the function based on a user-provided query response. Subject to this requirement, the user forms the query response so as to minimize the likelihood of the querier guessing a list of prescribed size to which the data value belongs based on the query response. We obtain a general converse upper bound for maximum list privacy. This bound is shown to be tight for the case of a binary-valued function through an explicit achievability scheme that involves an add-noise query response. |
| title | List Privacy Under Function Recoverability |
| topic | Information Theory |
| url | https://arxiv.org/abs/2307.05828 |